diff --git a/DESCRIPTION b/DESCRIPTION index af581d4..7fadaac 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -8,7 +8,7 @@ Description: The 'inti' package is part of the 'inkaverse' project for developin experiments and data collection (tarpuy()), data analysis and graphics (yupana()) , and technical writing. Learn more about the 'inkaverse' project at . -Date: 2022-07-29 +Date: 2022-08-08 Authors@R: c( person("Flavio", "Lozano-Isla", email = "flavjack@gmail.com", role = c("aut", "cre") , comment = c(ORCID = "0000-0002-0714-669X")), diff --git a/NEWS.md b/NEWS.md index fa7c100..aa13492 100644 --- a/NEWS.md +++ b/NEWS.md @@ -7,6 +7,9 @@ - Fix `plot_raw()`: "length(x) = 2 > 1' in coercion to 'logical(1)" - Update `jc_tombola()` - `outliers_remove(drop.na = FALSE)` allows avoid drop NA values by default + - `H2cal()` outliers are changed to NA in the data.frame + - `yupana_mvr()`: update function for correlation and PCA + - Yupana: update multivariate analysis # inti 0.5.6 diff --git a/R/H2cal.R b/R/H2cal.R index 60a1f9a..b884357 100644 --- a/R/H2cal.R +++ b/R/H2cal.R @@ -140,7 +140,6 @@ H2cal <- function(data ){ - # ------------------------------------------------------------------------- if (FALSE) { @@ -173,12 +172,15 @@ H2cal <- function(data out.rm <- data %>% outliers_remove(data = . , trait = trait , model = random.model + , drop_na = FALSE ) + dt.rm <- out.rm %>% purrr::pluck(1) out.fm <- data %>% outliers_remove(data = . , trait = trait , model = fixed.model + , drop_na = FALSE ) dt.fm <- out.fm %>% purrr::pluck(1) diff --git a/R/yupana_mvr.R b/R/yupana_mvr.R index 3c26ed7..53feba1 100644 --- a/R/yupana_mvr.R +++ b/R/yupana_mvr.R @@ -22,50 +22,16 @@ #' #' @export #' -#' @examples -#' -#' \dontrun{ -#' -#' library(inti) -#' library(gsheet) -#' -#' url <- paste0("https://docs.google.com/spreadsheets/d/" -#' , "15r7ZwcZZHbEgltlF6gSFvCTFA-CFzVBWwg3mFlRyKPs/edit#gid=172957346") -#' # browseURL(url) -#' -#' fb <- gsheet2tbl(url) -#' -#' mv <- yupana_mvr(data = fb -#' , last_factor = "bloque" -#' , summary_by = c("geno", "treat") -#' , groups = NULL -#' ) -#' -#' FactoMineR::plot.PCA(mv$pca, choix = "ind", habillage = mv$param$groups) -#' -#' } -#' yupana_mvr <- function(data , last_factor = NULL , summary_by = NULL , groups = NULL - , variables = NULL + , variables = NA ) { where <- NULL - if(FALSE) { - -data = fb -last_factor = NULL -summary_by = c("treat", "geno") -groups = "treat" -variables = NULL - - - } - # fieldbook structure ----------------------------------------------------- # ------------------------------------------------------------------------- @@ -77,9 +43,10 @@ variables = NULL {if(!is.null(last_factor)) mutate(.data = ., across(!c(1:{{last_factor}}), as.numeric)) else .} %>% select(where(~!all(is.na(.)))) %>% - {if(!is.null(variables)) - select(.data = ., {{summary_by}}, {{variables}}) else - select(.data = ., {{summary_by}}, where(is.numeric))} %>% + {if( "all" %in% variables || is.na(variables) ) + select(.data = ., {{summary_by}}, where(is.numeric)) else + select(.data = ., {{summary_by}}, {{variables}}) + } %>% group_by(across({{summary_by}})) %>% summarise(across(everything(), ~ mean(., na.rm = TRUE) )) %>% ungroup() %>% @@ -87,6 +54,8 @@ variables = NULL column_to_rownames("rnames") %>% as.data.frame() + # str(fb) + # parameters -------------------------------------------------------------- # ------------------------------------------------------------------------- @@ -99,43 +68,80 @@ variables = NULL , groups_n = groups_ncol ) +# condtions --------------------------------------------------------------- + + n <- fb %>% + select(par$quali) %>% + as.list() %>% + purrr::map(discard, is.na) %>% + lengths() %>% + prod() + + if(n <= 2) stop("The factors should have more than 2 levels") + # pca --------------------------------------------------------------------- # ------------------------------------------------------------------------- pca_info <- fb %>% select(where(~ length(unique(.)) > 1)) %>% # drop variables without variation - data.frame() %>% PCA(X = . , scale.unit = T , quali.sup = quali_ncol , graph = FALSE ) - # hcpc -------------------------------------------------------------------- - # ------------------------------------------------------------------------- + plot_pca_var <- FactoMineR::plot.PCA(x = pca_info + , choix = "var" + , autoLab = "auto" + , shadowtext = T + , graph.type = "ggplot" + ) + + legend <- if(nlevels(fb[[par$groups]]) > 20) "none" else "bottom" + + plot_pca_ind <- FactoMineR::plot.PCA(x = pca_info + , choice = "ind" + , habillage = par$groups_n + , invisible = "quali" + , autoLab = "auto" + , shadowtext = T + , graph.type = "ggplot" + ) + + theme(legend.position = legend) + +# hcpc -------------------------------------------------------------------- +# ------------------------------------------------------------------------- - clt_info <- HCPC(res = pca_info + clt_info <- FactoMineR::HCPC(res = pca_info , nb.clust = -1 , graph = FALSE ) - # Correlation ------------------------------------------------------------- - # ------------------------------------------------------------------------- - - cor <- fb %>% - select(where(is.numeric)) %>% - select(where(~ length(unique(.)) > 1)) %>% # drop variables without variation - agricolae::correlation(method = "pearson") + # plot.HCPC(x = clt_info + # , choice = "map" + # , legend = list(x = "topright" + # , cex = 0.6 + # , inset = 0.001 + # , box.lty=0 + # ) + # , draw.tree = F + # ) + +# plot -------------------------------------------------------------------- +# ------------------------------------------------------------------------- + +plots <- list(pca_var = plot_pca_var + , pca_ind = plot_pca_ind) - # results ----------------------------------------------------------------- - # ------------------------------------------------------------------------- +# results ----------------------------------------------------------------- +# ------------------------------------------------------------------------- - multvr = list( +multvr = list( pca = pca_info , hcpc = clt_info - , corr = cor , data = fb , param = par - ) + , plots = plots + ) } diff --git a/docs/articles/extra/files/fig-01.png b/docs/articles/extra/files/fig-01.png index a1b3224..a6ab9a8 100644 Binary files a/docs/articles/extra/files/fig-01.png and b/docs/articles/extra/files/fig-01.png differ diff --git a/docs/articles/extra/files/fig-03.png b/docs/articles/extra/files/fig-03.png index ecdface..468a6be 100644 Binary files a/docs/articles/extra/files/fig-03.png and b/docs/articles/extra/files/fig-03.png differ diff --git a/docs/articles/extra/files/plot_cluster_map.png b/docs/articles/extra/files/plot_cluster_map.png index 275ef8d..48aee64 100644 Binary files a/docs/articles/extra/files/plot_cluster_map.png and b/docs/articles/extra/files/plot_cluster_map.png differ diff --git a/docs/articles/extra/files/plot_pca_ind.png b/docs/articles/extra/files/plot_pca_ind.png index 47bc6c8..484df06 100644 Binary files a/docs/articles/extra/files/plot_pca_ind.png and b/docs/articles/extra/files/plot_pca_ind.png differ diff --git a/docs/articles/extra/files/plot_pca_var.png b/docs/articles/extra/files/plot_pca_var.png index fc2db20..dd3e9da 100644 Binary files a/docs/articles/extra/files/plot_pca_var.png and b/docs/articles/extra/files/plot_pca_var.png differ diff --git a/docs/articles/extra/yupana-coding.html b/docs/articles/extra/yupana-coding.html index a8d03ec..b0a563a 100644 --- a/docs/articles/extra/yupana-coding.html +++ b/docs/articles/extra/yupana-coding.html @@ -277,8 +277,8 @@

Leaf area## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 model$meancomp %>% web_table() -
- +
+

Tuber water use efficiency @@ -318,8 +318,8 @@

Tuber water use efficiency## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 model$meancomp %>% web_table()

-
- +
+

Plot in grids @@ -362,7 +362,8 @@

Multivariate analysis#> Principal component Analysis mv <- fb %>% - yupana_mvr(summary_by = c("geno", "treat") + yupana_mvr(last_factor = "bloque" + , summary_by = c("geno", "treat") , groups = "treat" ) diff --git a/docs/news/index.html b/docs/news/index.html index 0867f1e..75707e4 100644 --- a/docs/news/index.html +++ b/docs/news/index.html @@ -115,6 +115,13 @@

inti 0.5.7plot_raw(): “length(x) = 2 > 1’ in coercion to ’logical(1)”
  • Update jc_tombola()
    • +
  • +outliers_remove(drop.na = FALSE) allows avoid drop NA values by default
    • +
  • +H2cal() outliers are changed to NA in the data.frame
    • +
  • +yupana_mvr(): update function for correlation and PCA
    • +
  • Yupana: update multivariate analysis

    • inti 0.5.6

    CRAN release: 2022-05-19

    diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml index 051fc97..a2b0628 100644 --- a/docs/pkgdown.yml +++ b/docs/pkgdown.yml @@ -9,7 +9,7 @@ articles: rticles: rticles.html tarpuy: tarpuy.html yupana: yupana.html -last_built: 2022-07-17T18:24Z +last_built: 2022-08-08T15:52Z urls: reference: https://inkaverse.com/reference article: https://inkaverse.com/articles diff --git a/docs/reference/figure2qmd.html b/docs/reference/figure2qmd.html index df0f3e1..9d10cb8 100644 --- a/docs/reference/figure2qmd.html +++ b/docs/reference/figure2qmd.html @@ -131,7 +131,7 @@

    Value

    Details

    -

    Quarto option can be included in the title using "" separated by commas.

    +

    Quarto option can be included in the title using "" separated by commas

    +
    +

    Details

    +

    If you add "> END" will replace by "knitr::knit_exit()"

    +
    diff --git a/docs/reference/outliers_remove.html b/docs/reference/outliers_remove.html index 8cb73b0..bc51307 100644 --- a/docs/reference/outliers_remove.html +++ b/docs/reference/outliers_remove.html @@ -109,7 +109,7 @@

    Usage

    - 
    outliers_remove(data, trait, model)
    + 
    outliers_remove(data, trait, model, drop_na = TRUE)
    @@ -125,6 +125,10 @@

    Arguments

    Value

    @@ -152,12 +156,182 @@

    Examplesrmout <- outliers_remove( data = potato , trait ="stemdw" - , model = "0 + (1|bloque) + geno" + , model = "0 + treat*geno + (1|bloque) + geno" + , drop_na = FALSE ) - -rmout$outliers -#> bloque geno stemdw resi res_MAD rawp.BHStud index adjp bholm out_flag -#> 68 IV G05 80.65 60.36709 18.84505 0 68 0 0 OUTLIER + +rmout +#> $data +#> treat geno bloque stemdw +#> 1 sequia G01 II 14.87 +#> 2 sequia G02 IV 8.63 +#> 3 irrigado G01 III NA +#> 4 sequia G02 I 6.58 +#> 5 irrigado G03 II 12.63 +#> 6 irrigado G04 V 17.46 +#> 7 irrigado G01 I 15.32 +#> 8 irrigado G05 IV 14.55 +#> 9 sequia G06 II 21.19 +#> 10 sequia G05 I NA +#> 11 irrigado G01 II 18.13 +#> 12 sequia G07 II 3.70 +#> 13 irrigado G08 II 12.48 +#> 14 irrigado G06 III 29.49 +#> 15 irrigado G09 III 16.96 +#> 16 irrigado G10 II 8.20 +#> 17 sequia G11 I 7.90 +#> 18 sequia G12 III 9.19 +#> 19 irrigado G07 I 2.48 +#> 20 irrigado G04 II 20.75 +#> 21 irrigado G13 II 18.97 +#> 22 irrigado G14 III 14.57 +#> 23 irrigado G04 IV 18.84 +#> 24 sequia G04 V 8.79 +#> 25 sequia G08 V 8.17 +#> 26 sequia G04 III 12.53 +#> 27 sequia G01 IV 16.26 +#> 28 irrigado G10 I 11.19 +#> 29 irrigado G08 V 11.18 +#> 30 irrigado G02 V 12.14 +#> 31 irrigado G07 III 4.78 +#> 32 irrigado G08 I 12.52 +#> 33 irrigado G14 V 23.96 +#> 34 irrigado G03 I 11.18 +#> 35 sequia G13 III 7.79 +#> 36 sequia G01 V 11.97 +#> 37 sequia G03 I 9.03 +#> 38 irrigado G15 III 11.17 +#> 39 irrigado G03 IV 12.20 +#> 40 irrigado G09 IV 18.17 +#> 41 irrigado G11 II 4.90 +#> 42 sequia G03 V 8.73 +#> 43 sequia G11 III 5.56 +#> 44 irrigado G06 V 23.77 +#> 45 sequia G05 V NA +#> 46 sequia G08 IV 8.44 +#> 47 irrigado G11 IV 7.53 +#> 48 sequia G11 II 3.11 +#> 49 irrigado G10 III 14.77 +#> 50 sequia G06 IV 17.45 +#> 51 sequia G09 I 13.36 +#> 52 irrigado G11 I 7.27 +#> 53 sequia G11 IV 5.72 +#> 54 irrigado G15 IV 11.76 +#> 55 irrigado G13 IV 19.83 +#> 56 sequia G14 V 12.94 +#> 57 irrigado G02 IV 14.01 +#> 58 irrigado G09 II 19.20 +#> 59 irrigado G02 III 12.12 +#> 60 sequia G08 III 10.10 +#> 61 irrigado G06 II 24.35 +#> 62 sequia G13 IV 11.52 +#> 63 sequia G14 III 13.37 +#> 64 sequia G04 II 15.02 +#> 65 irrigado G11 III 10.32 +#> 66 irrigado G07 II 1.71 +#> 67 irrigado G08 IV 14.28 +#> 68 sequia G05 IV NA +#> 69 irrigado G04 I 12.80 +#> 70 irrigado G11 V 7.99 +#> 71 irrigado G12 I 19.60 +#> 72 sequia G14 IV 13.97 +#> 73 sequia G07 III 3.09 +#> 74 irrigado G03 III 8.56 +#> 75 sequia G01 I 10.44 +#> 76 sequia G04 I 13.73 +#> 77 sequia G03 II 8.33 +#> 78 irrigado G15 II 11.78 +#> 79 sequia G12 IV 12.30 +#> 80 sequia G12 I 13.91 +#> 81 sequia G08 I 5.14 +#> 82 sequia G05 II NA +#> 83 sequia G02 II 8.46 +#> 84 sequia G10 I 9.84 +#> 85 sequia G15 I 11.43 +#> 86 irrigado G07 V 1.71 +#> 87 sequia G10 V 6.36 +#> 88 sequia G13 II 12.34 +#> 89 sequia G07 V 2.71 +#> 90 sequia G03 III 7.16 +#> 91 sequia G15 IV 11.19 +#> 92 sequia G13 I 12.23 +#> 93 sequia G03 IV 8.37 +#> 94 irrigado G10 V 11.74 +#> 95 sequia G13 V 11.82 +#> 96 sequia G09 II 17.02 +#> 97 irrigado G14 IV 17.89 +#> 98 irrigado G01 V 13.80 +#> 99 sequia G01 III 15.37 +#> 100 irrigado G06 IV 33.52 +#> 101 sequia G04 IV 12.56 +#> 102 irrigado G15 V 12.13 +#> 103 irrigado G13 III 17.36 +#> 104 irrigado G02 II 12.58 +#> 105 sequia G08 II 10.31 +#> 106 irrigado G04 III 19.29 +#> 107 sequia G02 V 8.39 +#> 108 sequia G06 V 13.12 +#> 109 irrigado G15 I 12.14 +#> 110 irrigado G13 V 18.16 +#> 111 irrigado G05 V 12.03 +#> 112 sequia G09 III 16.71 +#> 113 sequia G09 V 10.97 +#> 114 sequia G10 II 7.44 +#> 115 irrigado G07 IV 4.06 +#> 116 irrigado G05 I 13.07 +#> 117 irrigado G02 I 8.54 +#> 118 sequia G05 III NA +#> 119 irrigado G12 II 17.81 +#> 120 sequia G15 III 10.95 +#> 121 irrigado G13 I 16.27 +#> 122 sequia G14 II 17.86 +#> 123 sequia G12 II 16.82 +#> 124 sequia G15 II 11.82 +#> 125 irrigado G09 V 14.22 +#> 126 sequia G06 I 16.22 +#> 127 sequia G09 IV 14.02 +#> 128 sequia G15 V 10.32 +#> 129 irrigado G14 I 19.93 +#> 130 sequia G06 III 17.45 +#> 131 irrigado G01 IV 16.97 +#> 132 irrigado G12 III 19.78 +#> 133 sequia G12 V 14.22 +#> 134 irrigado G12 V 17.61 +#> 135 sequia G11 V 3.95 +#> 136 irrigado G12 IV 19.87 +#> 137 irrigado G09 I 16.05 +#> 138 sequia G02 III 9.76 +#> 139 sequia G07 I 2.97 +#> 140 irrigado G08 III 11.61 +#> 141 irrigado G06 I 26.46 +#> 142 irrigado G10 IV NA +#> 143 irrigado G03 V 10.76 +#> 144 sequia G07 IV 0.97 +#> 145 irrigado G05 III 15.19 +#> 146 sequia G14 I 10.62 +#> 147 sequia G10 III 11.27 +#> 148 irrigado G14 II 17.86 +#> 149 irrigado G05 II 16.57 +#> 150 sequia G10 IV 6.58 +#> +#> $outliers +#> treat geno bloque stemdw resi res_MAD rawp.BHStud index +#> 3 irrigado G01 III 24.19 6.520276 4.031041 5.553035e-05 3 +#> 10 sequia G05 I 11.14 -13.467719 -8.326170 0.000000e+00 10 +#> 45 sequia G05 V 11.52 -13.006525 -8.041046 8.881784e-16 45 +#> 68 sequia G05 IV 80.65 54.860861 33.916722 0.000000e+00 68 +#> 82 sequia G05 II 11.65 -13.422893 -8.298457 0.000000e+00 82 +#> 118 sequia G05 III 10.02 -14.963724 -9.251048 0.000000e+00 118 +#> 142 irrigado G10 IV 5.03 -5.949139 -3.677946 2.351195e-04 142 +#> adjp bholm out_flag +#> 3 5.553035e-05 8.051901e-03 OUTLIER +#> 10 0.000000e+00 0.000000e+00 OUTLIER +#> 45 8.881784e-16 1.296740e-13 OUTLIER +#> 68 0.000000e+00 0.000000e+00 OUTLIER +#> 82 0.000000e+00 0.000000e+00 OUTLIER +#> 118 0.000000e+00 0.000000e+00 OUTLIER +#> 142 2.351195e-04 3.385720e-02 OUTLIER +#>

    diff --git a/docs/reference/yupana_mvr.html b/docs/reference/yupana_mvr.html index f320b24..4585035 100644 --- a/docs/reference/yupana_mvr.html +++ b/docs/reference/yupana_mvr.html @@ -111,7 +111,7 @@

    Usage last_factor = NULL, summary_by = NULL, groups = NULL, - variables = NULL + variables = NA ) @@ -149,32 +149,6 @@

    Details -
    -

    Examples

    - 
    
-if (FALSE) {
-
-library(inti)
-library(gsheet)
-
-url <- paste0("https://docs.google.com/spreadsheets/d/"
-              , "15r7ZwcZZHbEgltlF6gSFvCTFA-CFzVBWwg3mFlRyKPs/edit#gid=172957346")
-# browseURL(url)
-
-fb <- gsheet2tbl(url)
-
-mv <- yupana_mvr(data = fb
-                    , last_factor = "bloque"
-                    , summary_by = c("geno", "treat")
-                    , groups = NULL
-                    )
-
-FactoMineR::plot.PCA(mv$pca, choix = "ind", habillage =  mv$param$groups)
-
-}
-
-
    -
    diff --git a/docs/search.json b/docs/search.json index 9a321a2..c362d34 100644 --- a/docs/search.json +++ b/docs/search.json @@ -1 +1 @@ -[{"path":"https://inkaverse.com/articles/apps.html","id":"install-the-apps-locally","dir":"Articles","previous_headings":"","what":"Install the apps locally","title":"Apps","text":"case need change email account o renew credentials access apps can use googlesheets4::gs4_token().","code":""},{"path":"https://inkaverse.com/articles/apps.html","id":"tarpuy","dir":"Articles","previous_headings":"","what":"Tarpuy","title":"Apps","text":"Ease way deploy field-book experimental plans. demo options Tarpuy","code":""},{"path":"https://inkaverse.com/articles/apps.html","id":"yupana","dir":"Articles","previous_headings":"","what":"Yupana","title":"Apps","text":"Data analysis graphics experimental designs. demo options Yupana","code":""},{"path":"https://inkaverse.com/articles/apps.html","id":"huito","dir":"Articles","previous_headings":"","what":"Huito","title":"Apps","text":"open-source R package deploys flexible reproducible labels using layers. Huito Project","code":""},{"path":"https://inkaverse.com/articles/apps.html","id":"germinar-germinaquant","dir":"Articles","previous_headings":"","what":"GerminaR + GerminaQuant","title":"Apps","text":"GerminaR first platform base open source package calculate graphic germination indices R. GerminaR include web application called “GerminQuant R” non programming users. GerminaR Demo GerminaQuant Project","code":""},{"path":"https://inkaverse.com/articles/apps.html","id":"citation","dir":"Articles","previous_headings":"GerminaR + GerminaQuant","what":"Citation","title":"Apps","text":"Lozano-Isla, Flavio; Benites-Alfaro, Omar Eduardo; Pompelli, Marcelo Francisco (2019). GerminaR: R package germination analysis interactive web application “GerminaQuant R.” Ecological Research, 34(2), 339–346. https://doi.org/10.1111/1440-1703.1275","code":""},{"path":"https://inkaverse.com/articles/extra/stagewise.html","id":null,"dir":"Articles > Extra","previous_headings":"","what":"Load data","title":"Stagewise mixed-model analysis","text":"","code":"library(inti) library(purrr) library(dplyr) fb <- inti::met"},{"path":[]},{"path":"https://inkaverse.com/articles/extra/stagewise.html","id":null,"dir":"Articles > Extra","previous_headings":"","what":"asreml","title":"Stagewise mixed-model analysis","text":"","code":"library(asreml) library(data.table) library(plyr) library(stringr) asreml.options(maxit=100) # Set asreml iteration ############################ ##### Stage I LSMEANS ##### ##### per location ##### ww <- data.table(fb) ##### Make column Zone_Loc ##### trials <- nlevels(ww$env) envs <- levels(ww$env) ##### Make data list for Stage I ##### data_list <- matrix(data=list(), nrow=length(envs), ncol=1, dimnames=list(envs, c(\"data_Set\"))) ##### Make a list of Trials ##### for(i in 1:trials){ print(i) b <- levels(ww$env) c <- b[i] env <- as.factor(c) env <- data.table(env) f <- merge(ww,env,by=\"env\") assign(paste0(\"data_\", b[i]), f) data_list[[i, \"data_Set\" ]] <- f rm(b, c, f, env) } data_list <- data.table(ldply(data_list[, \"data_Set\"], data.frame, .id=\"env\")) stgI_list <- matrix(data=list(), nrow=length(envs), ncol=1, dimnames=list(envs, c(\"lsmeans\"))) asreml.options(maxit=100) # Set asreml iteration ############################ ##### Stage I LSMEANS ##### ##### per location ##### for (i in envs){ edat <- droplevels(subset(ww, env == i)) print(i) mod.1 <- asreml(fixed = yield ~ cultivar, random = ~ rep + rep:alpha, data = edat, predict = predict.asreml(classify = \"cultivar\")) update.asreml(mod.1) print(summary.asreml(mod.1)$varcomp) blue <- predict(mod.1, classify=\"cultivar\", levels=levels(edat$cultivar), vcov=TRUE,aliased = T) # get the lsmeans blue.1 <- data.table(blue$pvals)[, c(1:3)] names(blue.1) <- c(\"cultivar\", \"yield_lsm\", \"se\") blue.1[ , ':='(var=se^2, smith.w=diag(solve(blue$vcov)))] # calculate the Smith's weight stgI_list[[i, \"lsmeans\" ]] <- blue.1 # put all the results of Stage 1 in the list rm(Edat,mod.1, blue, blue.1) } ####################################################### ##### Preparing dataset of Stage I for Stage II ###### ##### Unlist the results of Stage I and format as data.table ##### stgII_list <- data.table(plyr::ldply(stgI_list[, \"lsmeans\"], data.frame, .id=\"env\")) stgII_list$zone<- factor(str_split_fixed(stgII_list$env, \"_\", 2)[,1]) # Make Zone column by split the record in Zone_Loc column stgII_list$location <- factor(str_split_fixed(stgII_list$env, \"_\", 3)[,2]) # Make Location by split the record in Zone_Loc column stgII_list$year <- factor(str_split_fixed(stgII_list$env, \"_\", 3)[,3]) # Make Year by split the record in Zone_Loc column blues.asreml <- stgII_list ############################ ##### Stage II BLUPs ###### ##### Zone analysis ##### model <- asreml(yield_lsm ~ zone, random = ~cultivar + zone:location + zone:cultivar + cultivar:zone:location, weights = smith.w, family = asr_gaussian(dispersion=1.0), # fix residual variance to 1 data = blues.asreml, predict = predict.asreml(classify = \"cultivar\") ) update.asreml(model) # print(summary.asreml(model)$varcomp) # print the variance components blups.asrml <- data.frame((model$predictions$pvals[1:4]))"},{"path":"https://inkaverse.com/articles/extra/stagewise.html","id":null,"dir":"Articles > Extra","previous_headings":"","what":"H2cal","title":"Stagewise mixed-model analysis","text":"Fixed model 0 + avoid intercep calculate BLUEs. emmeans = F calculate Smith weitghts first stage","code":"library(inti) library(purrr) #> First stage envs <- levels(fb$env) model <- 1:length(envs) %>% map(function(x) { model <- fb %>% filter(env %in% envs[x]) %>% H2cal(trait = \"yield\" , gen.name = \"cultivar\" , rep.n = 4 , fixed.model = \"0 + (1|rep) + (1|rep:alpha) + cultivar\" , random.model = \"1 + (1|rep) + (1|rep:alpha) + (1|cultivar)\" # , plot_diag = T , emmeans = F ) blues <- model$blues %>% mutate(trial = levels(fb$env)[x]) }) blues.h2cal <- bind_rows(model) %>% separate(trial, c(\"zone\", \"location\", \"year\")) %>% mutate(across(c(yield, smith.w), as.numeric)) %>% mutate(across(!c(yield, smith.w), as.factor)) #> Second stage met <- blues.h2cal %>% mutate(across(!yield, as.factor)) %>% H2cal(trait = \"yield\" , gen.name = \"cultivar\" , rep.n = 4 , env.n = 18 , env.name = \"location\" , fixed.model = \"0 + zone + (1|zone:location) + (1|zone:cultivar) + cultivar\" , random.model = \"1 + zone + (1|zone:location) + (1|zone:cultivar) + (1|cultivar)\" # , plot_diag = T , emmeans = T # , weights = blues.h2cal$smith.w ) blups.h2cal <- met$blups"},{"path":"https://inkaverse.com/articles/extra/stagewise.html","id":null,"dir":"Articles > Extra","previous_headings":"","what":"BLUEs comparison","title":"Stagewise mixed-model analysis","text":"BLUEs correlation H2Cal asrml (r = 1)","code":"blues.comp <- merge(blues.asreml , blues.h2cal , by = c(\"cultivar\", \"zone\", \"location\")) # plot(blues.comp$yield, blues.comp$yield_lsm) rs <- cor(blues.comp$yield, blues.comp$yield_lsm) cat(\"r =\", rs) ## r = 1 blues.comp %>% web_table(digits = 4)"},{"path":"https://inkaverse.com/articles/extra/stagewise.html","id":null,"dir":"Articles > Extra","previous_headings":"","what":"BLUPs comparison","title":"Stagewise mixed-model analysis","text":"BLUPs correlation H2Cal asrml (r = 0.9818724)","code":"blups.comp <- merge(blups.asrml, blups.h2cal , by = c(\"cultivar\")) # plot(blups.comp$yield, blups.comp$predicted.value) rs <- cor(blups.comp$yield, blups.comp$predicted.value) cat(\"r =\", rs) ## r = 0.9818724 blups.comp %>% web_table(digits = 4)"},{"path":[]},{"path":"https://inkaverse.com/articles/extra/stagewise.html","id":null,"dir":"Articles > Extra","previous_headings":"","what":"asreml","title":"Stagewise mixed-model analysis","text":"","code":"library(asreml) options(\"scipen\"=100,\"digits\"= 4 ) asreml.options(maxit=100) # Set asreml iteration ##### Fit a single-stage model ##### ## incomplete block and replicate location-specific ## location-specifice residual variance mod <- asreml(fixed = yield ~ zone, random = ~ rep:at(location) + rep:alpha:at(location) + zone:location + cultivar + cultivar:zone:location+ cultivar:zone, residual = ~ dsum(~(units)|location), data = fb, predict = predict.asreml(classify = \"cultivar\")) update.asreml(mod) blups.asreml <- data.frame((mod$predictions$pvals[1:4]))"},{"path":"https://inkaverse.com/articles/extra/stagewise.html","id":null,"dir":"Articles > Extra","previous_headings":"","what":"H2cal","title":"Stagewise mixed-model analysis","text":"","code":"library(inti) model <- fb %>% H2cal(trait = \"yield\" , gen.name = \"cultivar\" , env.name = \"location\" , rep.n = 2 , env.n = 18 , fixed.model = \"0 + zone + (1|rep:location) + (1|rep:alpha:location) + (1|zone:location) + (1|cultivar:zone) + (1|cultivar:zone:location) + cultivar\" , random.model = \"1 + zone + (1|rep:location) + (1|rep:alpha:location) + (1|zone:location) + (1|cultivar:zone) + (1|cultivar:zone:location) + (1|cultivar)\" , summary = T , emmeans = T # , plot_diag = T ) ## Linear mixed model fit by REML ['lmerMod'] ## Formula: yield ~ 1 + zone + (1 | rep:location) + (1 | rep:alpha:location) + ## (1 | zone:location) + (1 | cultivar:zone) + (1 | cultivar:zone:location) + ## (1 | cultivar) ## Data: dt.rm ## Weights: weights ## ## REML criterion at convergence: 11933.2 ## ## Scaled residuals: ## Min 1Q Median 3Q Max ## -5.0760 -0.3308 -0.0084 0.3698 4.0576 ## ## Random effects: ## Groups Name Variance Std.Dev. ## cultivar:zone:location (Intercept) 2209.5 47.00 ## rep:alpha:location (Intercept) 944.6 30.73 ## cultivar:zone (Intercept) 129.6 11.38 ## rep:location (Intercept) 334.0 18.28 ## cultivar (Intercept) 728.0 26.98 ## zone:location (Intercept) 48679.8 220.64 ## Residual 1396.8 37.37 ## Number of obs: 1069, groups: ## cultivar:zone:location, 539; rep:alpha:location, 251; cultivar:zone, 90; rep:location, 36; cultivar, 30; zone:location, 18 ## ## Fixed effects: ## Estimate Std. Error t value ## (Intercept) 813.35 83.85 9.700 ## zonenorth 51.57 129.67 0.398 ## zonesouth 59.75 123.21 0.485 ## ## Correlation of Fixed Effects: ## (Intr) znnrth ## zonenorth -0.644 ## zonesouth -0.678 0.439 blups.h2cal <- model$blups"},{"path":"https://inkaverse.com/articles/extra/stagewise.html","id":null,"dir":"Articles > Extra","previous_headings":"","what":"BLUPs comparison","title":"Stagewise mixed-model analysis","text":"BLUPs correlation H2Cal asrml (r = 0.9201732)","code":"blups.comp <- merge(blups.asreml, blups.h2cal, by = c(\"cultivar\")) # plot(blups.comp$predicted.value, blups.comp$yield) rs <- cor(blups.comp$predicted.value, blups.comp$yield) cat(\"r =\", rs) ## r = 0.9201732 blups.comp %>% web_table(digits = 4)"},{"path":[]},{"path":"https://inkaverse.com/articles/extra/yupana-coding.html","id":"packages","dir":"Articles > Extra","previous_headings":"","what":"Packages","title":"Yupana: coding workflow","text":"","code":"library(inti) library(gsheet) library(FactoMineR) library(cowplot) library(png)"},{"path":"https://inkaverse.com/articles/extra/yupana-coding.html","id":"import-data","dir":"Articles > Extra","previous_headings":"","what":"Import data","title":"Yupana: coding workflow","text":"","code":"url <- paste0(\"https://docs.google.com/spreadsheets/d/\" , \"15r7ZwcZZHbEgltlF6gSFvCTFA-CFzVBWwg3mFlRyKPs/edit#gid=172957346\") # browseURL(url) fb <- url %>% gsheet2tbl() %>% rename_with(tolower) %>% mutate(across(c(treat, geno, bloque), ~ as.factor(.))) %>% mutate(across(where(is.factor), ~ gsub(\"[[:space:]]\", \"\", .)) ) %>% as.data.frame() # str(fb)"},{"path":[]},{"path":"https://inkaverse.com/articles/extra/yupana-coding.html","id":"box-plot","dir":"Articles > Extra","previous_headings":"Plot raw data","what":"Box plot","title":"Yupana: coding workflow","text":"","code":"wue <- fb %>% plot_raw(type = \"boxplot\" , x = \"geno\" , y = \"wue\" , group = \"treat\" , xlab = \"Genotipos\" , ylab = \"Water use efficiency (g/l)\" , ylimits = c(5, 30, 5) , glab = \"Tratamientos\" )"},{"path":"https://inkaverse.com/articles/extra/yupana-coding.html","id":"scatter-plot","dir":"Articles > Extra","previous_headings":"Plot raw data","what":"Scatter plot","title":"Yupana: coding workflow","text":"","code":"hi <- fb %>% plot_raw(type = \"scatterplot\" , x = \"hi\" , y = \"twue\" , group = \"treat\" , xlab = \"Harvest Index\" , ylab = \"Tuber water use efficiency (g/l)\" , glab = \"Tratamientos\" )"},{"path":"https://inkaverse.com/articles/extra/yupana-coding.html","id":"plot-in-grids","dir":"Articles > Extra","previous_headings":"Plot raw data","what":"Plot in grids","title":"Yupana: coding workflow","text":"Water use effiency 15 potato genotypes : ) Box plot. B) Scatter plot","code":"grid <- plot_grid(wue, hi , nrow = 2 , labels = \"AUTO\") save_plot(\"files/fig-01.png\" , plot = grid , dpi= 300 , base_width = 10 , base_height = 10 , scale = 1.4 , units = \"cm\" ) fig <- include_figure( figure = \"files/fig-01.png\" , caption = \"Water use effiency in 15 potato genotypes\" , notes = \" A) Box plot. B) Scatter plot \" , label = \"__Where:__\" ) fig$figure"},{"path":[]},{"path":"https://inkaverse.com/articles/extra/yupana-coding.html","id":"leaf-area","dir":"Articles > Extra","previous_headings":"Plot summary data","what":"Leaf area","title":"Yupana: coding workflow","text":"","code":"#> Plot summary data model <- fb %>% yupana_analysis(response = \"lfa\" , model_factors = \"geno*treat\" , comparison = c(\"geno\", \"treat\") ) lfa <- model$meancomp %>% plot_smr(type = \"bar\" , x = \"geno\" , y = \"lfa\" , group = \"treat\" , ylimits = c(0, 12000, 2000) , sig = \"sig\" , error = \"ste\" , xlab = \"Genotipos\" , ylab = \"Area foliar (cm^2)\" , color = F ) model$anova %>% anova() ## Analysis of Variance Table ## ## Response: lfa ## Df Sum Sq Mean Sq F value Pr(>F) ## geno 14 261742780 18695913 33.371 < 0.00000000000000022 *** ## treat 1 788562704 788562704 1407.541 < 0.00000000000000022 *** ## geno:treat 14 108153220 7725230 13.789 < 0.00000000000000022 *** ## Residuals 120 67228987 560242 ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 model$meancomp %>% web_table()"},{"path":"https://inkaverse.com/articles/extra/yupana-coding.html","id":"tuber-water-use-efficiency","dir":"Articles > Extra","previous_headings":"Plot summary data","what":"Tuber water use efficiency","title":"Yupana: coding workflow","text":"","code":"model <- fb %>% yupana_analysis(response = \"twue\" , model_factors = \"block + geno*treat\" , comparison = c(\"geno\", \"treat\") ) twue <- model$meancomp %>% plot_smr(type = \"line\" , x = \"geno\" , y = \"twue\" , group = \"treat\" , ylimits = c(0, 10, 2) , error = \"ste\" , color = c(\"blue\", \"red\") , ) + labs(x = \"Genotipos\" , y = \"Tuber water use effiency (g/l)\" ) model$anova %>% anova() ## Analysis of Variance Table ## ## Response: twue ## Df Sum Sq Mean Sq F value Pr(>F) ## block 1 20.78 20.7770 31.0214 0.0000001609 *** ## geno 14 413.06 29.5046 44.0523 < 0.00000000000000022 *** ## treat 1 2.04 2.0370 3.0414 0.08375 . ## geno:treat 14 16.07 1.1479 1.7140 0.06138 . ## Residuals 119 79.70 0.6698 ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 model$meancomp %>% web_table()"},{"path":"https://inkaverse.com/articles/extra/yupana-coding.html","id":"plot-in-grids-1","dir":"Articles > Extra","previous_headings":"Plot summary data","what":"Plot in grids","title":"Yupana: coding workflow","text":"Water use effiency 15 potato genotypes : ) Bar plot B) Line plot","code":"grid <- plot_grid(lfa, twue , nrow = 2 , labels = \"AUTO\") ggsave2(\"files/fig-02.png\" , plot = grid , dpi= 300 , width = 10 , height = 10 , scale = 1.5 , units = \"cm\") fig <- include_figure( figure = \"files/fig-02.png\" , caption = \"Water use effiency in 15 potato genotypes\" , notes = \" A) Bar plot B) Line plot \" , label = \"__Where:__\" ) fig$figure"},{"path":"https://inkaverse.com/articles/extra/yupana-coding.html","id":"multivariate-analysis","dir":"Articles > Extra","previous_headings":"","what":"Multivariate analysis","title":"Yupana: coding workflow","text":"Multivariate Analysis: Principal component analysis hierarchical clustering analysis.","code":"#> Principal component Analysis mv <- fb %>% yupana_mvr(summary_by = c(\"geno\", \"treat\") , groups = \"treat\" ) # sink(\"files/pca.txt\") # # Results # summary(pca, nbelements = Inf, nb.dec = 2) # # Correlation de dimensions # dimdesc(pca) # sink() ppi <- 300 png(\"files/plot_pca_var.png\", width=7*ppi, height=7*ppi, res=ppi) plot.PCA(mv$pca, choix=\"var\", title=\"\", autoLab = \"y\", cex = 0.8, shadowtext = T) graphics.off() ppi <- 300 png(\"files/plot_pca_ind.png\", width=7*ppi, height=7*ppi, res=ppi) plot.PCA(mv$pca, choix=\"ind\", habillage = 2, title=\"\", autoLab = \"y\", cex = 0.8, shadowtext = T, label = \"ind\", legend = list(bty = \"y\", x = \"topright\")) graphics.off() # Hierarchical Clustering Analysis clt <- mv$pca %>% HCPC(., nb.clust=-1, graph = F) # sink(\"files/clu.txt\") # clus$call$t$tree # clus$desc.ind # clus$desc.var # sink() ppi <- 300 png(\"files/plot_cluster_tree.png\", width=7*ppi, height=7*ppi, res=ppi) plot.HCPC(x = clt, choice = \"tree\") graphics.off() ppi <- 300 png(\"files/plot_cluster_map.png\", width=7*ppi, height=7*ppi, res=ppi) plot.HCPC(x = clt, choice = \"map\") graphics.off() plot.01 <- readPNG(\"files/plot_pca_var.png\") %>% grid::rasterGrob() plot.02 <- readPNG(\"files/plot_pca_ind.png\") %>% grid::rasterGrob() plot.03 <- readPNG(\"files/plot_cluster_map.png\") %>% grid::rasterGrob() plot.04 <- readPNG(\"files/plot_cluster_tree.png\") %>% grid::rasterGrob() plot <- plot_grid(plot.01, plot.02, plot.03, plot.04 , nrow = 2 , labels = \"AUTO\") ggsave2(\"files/fig-03.png\" , plot = plot , dpi = 300 , width = 12 , height = 10 , scale = 1.5 , units = \"cm\") fig <- include_figure( caption = \"Multivariate Analysis: Principal component analysis and hierarchical clustering analysis.\" , figure = \"files/fig-03.png\" ) fig$figure"},{"path":"https://inkaverse.com/articles/heritability.html","id":"broad-sense-heritability-h2","dir":"Articles","previous_headings":"","what":"Broad-sense heritability (\\(H^2\\))","title":"Broad-sense heritability in plant breeding","text":"Broad-sense heritability (\\(H^2\\)) defined proportion phenotypic variance attributable overall genetic variance genotype (Schmidt et al., 2019b). usually additional interpretations associated \\(H^2\\): () equivalent coefficient determination linear regression unobservable genotypic value observed phenotype; (ii) also squared correlation predicted phenotypic value genotypic value; (iii) represents proportion selection differential (\\(S\\)) can realized response selection (\\(R\\)) (Falconer Mackay, 2005). two main reasons heritability entry-mean basis interest plant breeding (Schmidt et al., 2019a): plugged breeder’s Equation predict response selection. descriptive measure used assess usefulness precision results cultivar evaluation trials.","code":""},{"path":"https://inkaverse.com/articles/heritability.html","id":"breeders-equation","dir":"Articles","previous_headings":"Broad-sense heritability (\\(H^2\\))","what":"Breeder´s equation","title":"Broad-sense heritability in plant breeding","text":"\\[\\Delta G=H^2S\\] : \\(\\Delta G\\) genetic gain \\(S\\) mean phenotypic value selected genotypes, expressed deviation population mean.","code":""},{"path":"https://inkaverse.com/articles/heritability.html","id":"usual-problems","dir":"Articles","previous_headings":"","what":"Usual Problems","title":"Broad-sense heritability in plant breeding","text":"practice, trials conducted multienvironment trial (MET) presente unbalanced data cultivars tested environment simply plot data lost number replicates location varies genotypes (Schmidt et al., 2019b). However, standard method estimating heritability implicitly assumes balanced data, independent genotype effects, homogeneous variances.","code":""},{"path":"https://inkaverse.com/articles/heritability.html","id":"how-calculate-the-heritability","dir":"Articles","previous_headings":"","what":"How calculate the Heritability?","title":"Broad-sense heritability in plant breeding","text":"According Schmidt et al. (2019a), variance components calculated two ways:","code":""},{"path":"https://inkaverse.com/articles/heritability.html","id":"two-stages-approach","dir":"Articles","previous_headings":"How calculate the Heritability?","what":"1) Two stages approach","title":"Broad-sense heritability in plant breeding","text":"two stage approach, first stage experiment analyzed individually according experiment design (Lattice, CRBD, etc) (Zystro et al., 2018). second stage environments denotes year--location interaction. approach assumes single variance genotype--environment interactions (GxE), even multiple locations tested across multiple years (Buntaran et al., 2020).","code":""},{"path":"https://inkaverse.com/articles/heritability.html","id":"model","dir":"Articles","previous_headings":"How calculate the Heritability? > 1) Two stages approach","what":"Model","title":"Broad-sense heritability in plant breeding","text":"\\[y_{ikt}=\\mu\\ +\\ G_i+E_t+GxE_{}+\\varepsilon_{ikt}\\]","code":""},{"path":"https://inkaverse.com/articles/heritability.html","id":"phenotypic-variance","dir":"Articles","previous_headings":"How calculate the Heritability? > 1) Two stages approach","what":"Phenotypic variance","title":"Broad-sense heritability in plant breeding","text":"\\[\\sigma_p^2=\\sigma_g^2+\\frac{\\sigma_{g\\cdot e}^2}{n_e}+\\frac{\\sigma_{\\varepsilon}^2}{n_e\\cdot n_r}\\]","code":""},{"path":"https://inkaverse.com/articles/heritability.html","id":"one-stage-approach","dir":"Articles","previous_headings":"How calculate the Heritability?","what":"2) One stage approach","title":"Broad-sense heritability in plant breeding","text":"one stage approach one model used MET analysis. environmental effects included via separate year, location main interaction effects. \\[y_{ikt}=\\mu+G_i+Y_m+E_q+YxE_{mq}+GxY_{im}+GxE_{iq}+GxYxE_{imq}+\\varepsilon_{ikmq}\\]","code":""},{"path":"https://inkaverse.com/articles/heritability.html","id":"phenotypic-variance-1","dir":"Articles","previous_headings":"How calculate the Heritability? > 2) One stage approach","what":"Phenotypic variance","title":"Broad-sense heritability in plant breeding","text":"\\[\\sigma_p^2=\\sigma_g^2+\\frac{\\sigma_{g\\cdot e}^2}{n_e}+\\frac{\\sigma_{g\\cdot y}^2}{n_y}+\\frac{\\sigma_{g\\cdot y\\cdot e}^2}{n_y\\cdot n_e}+\\ \\frac{\\sigma_{\\epsilon}^2}{n_e\\cdot n_y\\cdot n_r}\\]","code":""},{"path":"https://inkaverse.com/articles/heritability.html","id":"differentes-heritability-calculations","dir":"Articles","previous_headings":"","what":"Differentes heritability calculations","title":"Broad-sense heritability in plant breeding","text":"Table 1: Differentes heritability calculation","code":""},{"path":"https://inkaverse.com/articles/heritability.html","id":"heritability-function-in-the-package","dir":"Articles","previous_headings":"","what":"Heritability function in the package","title":"Broad-sense heritability in plant breeding","text":"calculate standard heritability MET experiments number location replication include manually function H2cal(). case difference number replication experiments, take maximum value (often done practice) (Schmidt et al., 2019b). remove outliers function implemented Method 4 used Bernal-Vasquez et al. (2016): Bonferroni-Holm using re-scaled MAD standardizing residuals (BH-MADR).","code":""},{"path":"https://inkaverse.com/articles/heritability.html","id":"load-packages","dir":"Articles","previous_headings":"Heritability function in the package","what":"Load packages","title":"Broad-sense heritability in plant breeding","text":"","code":"library(inti)"},{"path":"https://inkaverse.com/articles/heritability.html","id":"h2cal-function","dir":"Articles","previous_headings":"Heritability function in the package","what":"H2cal function","title":"Broad-sense heritability in plant breeding","text":"","code":"dt <- potato hr <- H2cal(data = dt , trait = \"stemdw\" , gen.name = \"geno\" , rep.n = 5 , fixed.model = \"0 + (1|bloque) + geno\" , random.model = \"1 + (1|bloque) + (1|geno)\" , emmeans = TRUE , plot_diag = TRUE , outliers.rm = TRUE )"},{"path":"https://inkaverse.com/articles/heritability.html","id":"model-information","dir":"Articles","previous_headings":"Heritability function in the package","what":"Model information","title":"Broad-sense heritability in plant breeding","text":"","code":"hr$model %>% summary() ## Linear mixed model fit by REML ['lmerMod'] ## Formula: stemdw ~ 1 + (1 | bloque) + (1 | geno) ## Data: dt.rm ## Weights: weights ## ## REML criterion at convergence: 796.1 ## ## Scaled residuals: ## Min 1Q Median 3Q Max ## -2.38440 -0.64247 -0.08589 0.57452 2.84508 ## ## Random effects: ## Groups Name Variance Std.Dev. ## geno (Intercept) 19.960 4.4677 ## bloque (Intercept) 0.110 0.3316 ## Residual 9.411 3.0677 ## Number of obs: 148, groups: geno, 15; bloque, 5 ## ## Fixed effects: ## Estimate Std. Error t value ## (Intercept) 12.51 1.19 10.51"},{"path":"https://inkaverse.com/articles/heritability.html","id":"variance-components","dir":"Articles","previous_headings":"Heritability function in the package","what":"Variance components","title":"Broad-sense heritability in plant breeding","text":"Table 2: Variance component table","code":"hr$tabsmr %>% kable(caption = \"Variance component table\")"},{"path":"https://inkaverse.com/articles/heritability.html","id":"best-linear-unbiased-estimators-blues","dir":"Articles","previous_headings":"Heritability function in the package","what":"Best Linear Unbiased Estimators (BLUEs)","title":"Broad-sense heritability in plant breeding","text":"Table 3: BLUEs","code":"hr$blues %>% kable(caption = \"BLUEs\")"},{"path":"https://inkaverse.com/articles/heritability.html","id":"best-linear-unbiased-predictors-blups","dir":"Articles","previous_headings":"Heritability function in the package","what":"Best Linear Unbiased Predictors (BLUPs)","title":"Broad-sense heritability in plant breeding","text":"Table 4: BLUPs","code":"hr$blups %>% kable(caption = \"BLUPs\")"},{"path":"https://inkaverse.com/articles/heritability.html","id":"outliers","dir":"Articles","previous_headings":"Heritability function in the package","what":"Outliers","title":"Broad-sense heritability in plant breeding","text":"Table 5: Outliers fixed model Table 6: Outliers random model","code":"hr$outliers$fixed %>% kable(caption = \"Outliers fixed model\") hr$outliers$random %>% kable(caption = \"Outliers random model\")"},{"path":"https://inkaverse.com/articles/heritability.html","id":"comparison-h2cal-and-asreml","dir":"Articles","previous_headings":"","what":"Comparison: H2cal and asreml","title":"Broad-sense heritability in plant breeding","text":"https://inkaverse.com/articles/extra/stagewise.html","code":""},{"path":"https://inkaverse.com/articles/policy.html","id":"privacy-policy-for-apps-that-access-google-apis","dir":"Articles","previous_headings":"","what":"Privacy policy for apps that access Google APIs","title":"Inkaverse Privacy Policy","text":"Inkaverse maintains several web apps make easier work Google APIs R: Yupana wraps Sheets API Tarpuy wraps Sheets API apps governed common policies recorded . apps use internal resources owned “inkaverse” project Google Cloud Platform. name see consent screen. Exception: gmailr use resources owned inkaverse Package, due special requirements around Gmail scopes. use Google APIs apps subject API’s respective terms service. See https://developers.google.com/terms/.","code":""},{"path":[]},{"path":[]},{"path":"https://inkaverse.com/articles/policy.html","id":"accessing-user-data","dir":"Articles","previous_headings":"Privacy > Google account and user data","what":"Accessing user data","title":"Inkaverse Privacy Policy","text":"applications access Google resources local machine web. machine communicates directly Google APIs. inkaverse API Packages project never receives data permission access data. owners project can see anonymous, aggregated information usage tokens obtained OAuth client, APIs endpoints used. package includes functions can execute order read modify data. can happen provide token, requires authenticate specific Google user authorize actions. package can help get token guiding OAuth flow browser. must consent allow inkaverse API Packages operate behalf. OAuth consent screen describe scope authorized, e.g., name target API(s) whether authorizing “read ” “read write” access. two ways use apps without authorizing inkaverse API Packages: bring service account token configure package use OAuth client choice.","code":""},{"path":"https://inkaverse.com/articles/policy.html","id":"scopes","dir":"Articles","previous_headings":"Privacy > Google account and user data","what":"Scopes","title":"Inkaverse Privacy Policy","text":"Overview scopes requested various inkaverse API Packages rationale: Sheets (read/write): googlesheets4 package used apps allows manage spreadsheets therefore default scopes include read/write access. googlesheets4 package makes possible get token limited scope, e.g. read .","code":""},{"path":"https://inkaverse.com/articles/policy.html","id":"sharing-user-data","dir":"Articles","previous_headings":"Privacy > Google account and user data","what":"Sharing user data","title":"Inkaverse Privacy Policy","text":"package communicate Google APIs. user data shared owners inkaverse API Package servers.","code":""},{"path":"https://inkaverse.com/articles/policy.html","id":"storing-user-data","dir":"Articles","previous_headings":"Privacy > Google account and user data","what":"Storing user data","title":"Inkaverse Privacy Policy","text":"package may store credentials local machine, later reuse . Use caution using packages shared machine. default, OAuth token cached local file, ~/.R/gargle/gargle-oauth. See documentation gargle::gargle_options() gargle::credentials_user_oauth2() information control location token cache suppress token caching, globally individual token level.","code":""},{"path":"https://inkaverse.com/articles/policy.html","id":"policies-for-authors-of-packages-or-other-applications","dir":"Articles","previous_headings":"","what":"Policies for authors of packages or other applications","title":"Inkaverse Privacy Policy","text":"use API key client ID inkaverse API Packages external package tool. Per Google User Data Policy https://developers.google.com/terms/api-services-user-data-policy, application must accurately represent authenticating Google API services. use inkaverse package inside another package application executes logic — opposed code inkaverse API Packages user — must communicate clearly user. use credentials inkaverse API Package; instead, use credentials associated project user.","code":""},{"path":"https://inkaverse.com/articles/rticles.html","id":"herramientas-para-documentos-reproducibles","dir":"Articles","previous_headings":"","what":"Herramientas para documentos reproducibles","title":"Rticles","text":"Para la construcción de documentos técnico/científicos con R, deben crearse algunas cuentas e instalar los programas que necesitamos. Estas herramientas son independientes del sistema operativo, de acceso libre y pueden ser usadas para la investigación reproducible. El listado de herramientas son una recomendación basada en mi experiencia, y son las únicas herramientas.","code":""},{"path":"https://inkaverse.com/articles/rticles.html","id":"cuentas","dir":"Articles","previous_headings":"Herramientas para documentos reproducibles","what":"Cuentas","title":"Rticles","text":"Se recomienda usar el mismo correo para todas las cuentas. El uso de correos diferentes para cada servicio dificultará el flujo de trabajo. Deben crearse una cuenta en los siguientes servicios: Google (Gmail). Se recomienda que tengan una cuenta de Google ya que nos permitirá tener acceso Gsuit que posee un conjunto de herramientas gratuitas en línea. Estas herramientas son un buen complemento para el trabajo en equipo y puedes acceder ellos desde distintos dispositivos móviles. Zotero. Será nuestra biblioteca virtual, y una de las herramientas que más usaremos, ya que nos permitirá organizar nuestro trabajo y citar los documentos en nuestros manuscritos. GitHub. Es un servicio de repositorio de código. Nos ayudará organizar nuestros proyectos y códigos. Nos permite visualizar los historiales de cambio de nuestro proyecto, compartir nuestro código y generar páginas webs para publicar documentos en línea.","code":""},{"path":[]},{"path":"https://inkaverse.com/articles/rticles.html","id":"programas","dir":"Articles","previous_headings":"Herramientas para documentos reproducibles","what":"Programas","title":"Rticles","text":"Instalar los siguientes programas en el orden que se mencionan, para evitar conflictos en su funcionamiento. Zotero. Es un gestor de referencias bibliográficas, libre, abierto y gratuito desarrollado por el Center History New Media de la Universidad George Mason. R CRAN. Es un entorno de lenguaje de programación con un enfoque al análisis estadístico. El software R viene por defecto con funcionalidades básicas y para ampliar estas debemos instalar paquetes. R actualmente nos permite hacer distintas tareas comó análisis estadísticos, generación de gráficos, escritura de documentos, desarrollo de aplicaciones webs, etc. RStudio. RStudio es un entorno de desarrollo integrado para el lenguaje de programación R, dedicado la computación estadística y gráficos. Git. Git es un software de control de versiones. Esta pensando en la eficiencia y la confiabilidad del mantenimiento de versiones de aplicaciones. Git nos permitirá usar bash en windows través del terminal en RStudio.","code":""},{"path":[]},{"path":[]},{"path":"https://inkaverse.com/articles/rticles.html","id":"herramientas-adicionales","dir":"Articles","previous_headings":"Herramientas para documentos reproducibles","what":"Herramientas adicionales","title":"Rticles","text":"Existen alguna herramientas básicas que deben faltar en tú computador: Chrome (buscador web) Foxit Reader (lector de PDFs) WinRAR (compression/descompresor de archivos) Google Backup Sync (servicio de sincronización de datos) ShareX (herramienta para captura de pantalla) Los usuarios de Windows, pueden instalar estas aplicaciones entre otras desde ninite.","code":""},{"path":"https://inkaverse.com/articles/rticles.html","id":"chocolatey-opcional","dir":"Articles","previous_headings":"Herramientas para documentos reproducibles","what":"Chocolatey (opcional)","title":"Rticles","text":"Si eres usuario de windows, puedes instalar todas las herramientas mencionadas desde el administrador de paquetes chocolatey través de PowerShell.","code":"open https://chocolatey.org/packages Start-Process powershell -Verb runAs Set-ExecutionPolicy Bypass -Scope Process -Force; [System.Net.ServicePointManager]::SecurityProtocol = [System.Net.ServicePointManager]::SecurityProtocol -bor 3072; iex ((New-Object System.Net.WebClient).DownloadString('https://chocolatey.org/install.ps1')) choco install avastfreeantivirus choco install googlechrome choco install winrar choco install zotero choco install r choco install rtools choco install r.studio choco install git choco install google-backup-and-sync choco install foxitreader choco install sharex choco install k-litecodecpackfull choco install gom-player choco install aimp choco install teamviewer"},{"path":"https://inkaverse.com/articles/tarpuy.html","id":"módulos","dir":"Articles","previous_headings":"","what":"Módulos","title":"Tarpuy","text":"Módulos de la aplicación Tarpuy","code":""},{"path":"https://inkaverse.com/articles/yupana.html","id":"base-de-datos","dir":"Articles","previous_headings":"","what":"Base de datos","title":"Yupana","text":"Los datos deben estar organizado en formato tidy-data. Tener en cuenta algunas consideraciones: usar caracteres extraños en la cabeceras, e..: %, #, &, $, °, !, ^, etc Los datos deben iniciar en la primera fila y columna, e.. A1 Evitar usar espacio entre los nombres de las variables, en reemplazo pueden usar “_” o “.” Las columnas que esten entre corchetes “[]” serán excluidas del análisis","code":""},{"path":"https://inkaverse.com/articles/yupana.html","id":"módulos","dir":"Articles","previous_headings":"","what":"Módulos","title":"Yupana","text":"Table 1: Módulos de la aplicación Yupana","code":""},{"path":"https://inkaverse.com/articles/yupana.html","id":"graphics","dir":"Articles","previous_headings":"","what":"Graphics","title":"Yupana","text":"Los parámetros de los gráficos generados en la app pueden ser guardadas en hojas de cálculo de google y luego pueden ser cargadas (Table 2).","code":""},{"path":"https://inkaverse.com/articles/yupana.html","id":"opciones-de-gráfico","dir":"Articles","previous_headings":"Graphics","what":"Opciones de gráfico","title":"Yupana","text":"Table 2: Lista de argumentos, descripción y opciones para la generación de gráficos en la aplicación Yupana Nota: Opciones basadas en la función: plot_smr()","code":""},{"path":"https://inkaverse.com/articles/yupana.html","id":"argumentos-y-valores","dir":"Articles","previous_headings":"Graphics > Opciones de gráfico","what":"Argumentos y valores","title":"Yupana","text":"Figure 1: Parámetros en {arguments} y {values} para la generación de gráficos en la aplicación Yupana. Figure 2: Figura basada en los {arguments} y {values} de la tabla anterior. La apliación por defecto genera un gama de colores {colors} en una escala de grises. Los colores pueden ser modificados de forma manual por sus nombres en ingles o usando los valores HEX. En este caso se cambió la escala de grises por los colores verde (green) y rojo (red) (Figure 1, 2).","code":""},{"path":"https://inkaverse.com/articles/yupana.html","id":"incluir-nuevas-capas-opt","dir":"Articles","previous_headings":"Graphics","what":"Incluir nuevas capas opt","title":"Yupana","text":"Yupana partir de la versión 0.2.0 permite la inclusión de capas adicionales los gráficos. Puedes incluir dicha información en opt de los {arguments} (Figure 3, 4). Puedes incluir diversas capas descritas para el paquete ggplot2. Figure 3: Gráfico con la inclusión de la capa facet_grid() Figure 4: Inclusión de facet_grid(tratamiento ~ .) en opt de los {arguments} en Yupana.","code":""},{"path":"https://inkaverse.com/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Flavio Lozano-Isla. Author, maintainer. . Contributor. . Copyright holder.","code":""},{"path":"https://inkaverse.com/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Lozano-Isla F (2022). inti: Tools Statistical Procedures Plant Science. R package version 0.5.6, https://CRAN.R-project.org/package=inti.","code":"@Manual{, title = {{inti}: Tools and Statistical Procedures in Plant Science}, author = {Flavio Lozano-Isla}, year = {2022}, note = {R package version 0.5.6}, url = {https://CRAN.R-project.org/package=inti}, }"},{"path":"https://inkaverse.com/index.html","id":"inti-","dir":"","previous_headings":"","what":"Inkaverse","title":"Inkaverse","text":"‘inti’ package part ‘inkaverse’ project developing different procedures tools used plant science experimental designs. mean aim package support researchers planning experiments data collection ‘tarpuy()’, data analysis graphics ‘yupana()’, technical writing. Learn ‘inkaverse’ project https://inkaverse.com/.","code":""},{"path":"https://inkaverse.com/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Inkaverse","text":"install stable version CRAN: install latest development version directly GitHub: need install specific version:","code":"install.packages(\"inti\") if (!require(\"remotes\")) install.packages(\"remotes\") remotes::install_github(\"flavjack/inti\") if (!require(\"remotes\")) install.packages(\"remotes\") remotes::install_version(\"inti\", version = \"0.4.4\")"},{"path":"https://inkaverse.com/index.html","id":"shiny-apps","dir":"","previous_headings":"","what":"Shiny apps","title":"Inkaverse","text":"first time running apps consider install app dependencies: install package app dependencies also can access apps Addins list Rstudio running following code:","code":"inti::yupana(dependencies = TRUE)"},{"path":"https://inkaverse.com/index.html","id":"yupana","dir":"","previous_headings":"Shiny apps","what":"Yupana","title":"Inkaverse","text":"","code":"inti::yupana()"},{"path":"https://inkaverse.com/index.html","id":"tarpuy","dir":"","previous_headings":"Shiny apps","what":"Tarpuy","title":"Inkaverse","text":"","code":"inti::tarpuy()"},{"path":"https://inkaverse.com/reference/colortext.html","id":null,"dir":"Reference","previous_headings":"","what":"Colourise text for display in the terminal — colortext","title":"Colourise text for display in the terminal — colortext","text":"R currently running system supports terminal colours text returned unchanged.","code":""},{"path":"https://inkaverse.com/reference/colortext.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Colourise text for display in the terminal — colortext","text":"","code":"colortext(text, fg = \"red\", bg = NULL)"},{"path":"https://inkaverse.com/reference/colortext.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Colourise text for display in the terminal — colortext","text":"text character vector fg foreground colour, defaults white bg background colour, defaults transparent","code":""},{"path":"https://inkaverse.com/reference/colortext.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Colourise text for display in the terminal — colortext","text":"Allowed colours : black, blue, brown, cyan, dark gray, green, light blue, light cyan, light gray, light green, light purple, light red, purple, red, white, yellow","code":""},{"path":"https://inkaverse.com/reference/colortext.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Colourise text for display in the terminal — colortext","text":"testthat package","code":""},{"path":"https://inkaverse.com/reference/colortext.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Colourise text for display in the terminal — colortext","text":"","code":"print(colortext(\"Red\", \"red\")) #> [1] \"\\033[0;31mRed\\033[0m\" cat(colortext(\"Red\", \"red\"), \"\\n\") #> Red cat(colortext(\"White on red\", \"white\", \"red\"), \"\\n\") #> White on red"},{"path":"https://inkaverse.com/reference/figure2qmd.html","id":null,"dir":"Reference","previous_headings":"","what":"Figure to Rmarkdown — figure2qmd","title":"Figure to Rmarkdown — figure2qmd","text":"Use Articul8 Add-ons Google docs build Rticles","code":""},{"path":"https://inkaverse.com/reference/figure2qmd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Figure to Rmarkdown — figure2qmd","text":"","code":"figure2qmd(text, path = \".\", opts = NA)"},{"path":"https://inkaverse.com/reference/figure2qmd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Figure to Rmarkdown — figure2qmd","text":"text Markdown text figure information [string] path Image path figures [path: \".\" (base directory)] opts chunk options brackets [string: NA]","code":""},{"path":"https://inkaverse.com/reference/figure2qmd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Figure to Rmarkdown — figure2qmd","text":"string mutated","code":""},{"path":"https://inkaverse.com/reference/figure2qmd.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Figure to Rmarkdown — figure2qmd","text":"Quarto option can included title using \"\" separated commas.","code":""},{"path":"https://inkaverse.com/reference/figure2rmd.html","id":null,"dir":"Reference","previous_headings":"","what":"Figure to Rmarkdown — figure2rmd","title":"Figure to Rmarkdown — figure2rmd","text":"Use Articul8 Add-ons Google docs build Rticles","code":""},{"path":"https://inkaverse.com/reference/figure2rmd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Figure to Rmarkdown — figure2rmd","text":"","code":"figure2rmd(text, path = \".\", opts = NA, prefix = \"Figure\")"},{"path":"https://inkaverse.com/reference/figure2rmd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Figure to Rmarkdown — figure2rmd","text":"text String table information path Path image fot figure opts chunk options brackets. prefix Prefix name figure","code":""},{"path":"https://inkaverse.com/reference/figure2rmd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Figure to Rmarkdown — figure2rmd","text":"Mutated string","code":""},{"path":"https://inkaverse.com/reference/footnotes.html","id":null,"dir":"Reference","previous_headings":"","what":"Footnotes in tables — footnotes","title":"Footnotes in tables — footnotes","text":"Include tables footnotes symbols kables pandoc format","code":""},{"path":"https://inkaverse.com/reference/footnotes.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Footnotes in tables — footnotes","text":"","code":"footnotes(table, notes = NULL, label = \"Note:\", notation = \"alphabet\")"},{"path":"https://inkaverse.com/reference/footnotes.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Footnotes in tables — footnotes","text":"table Kable output pandoc format. notes Footnotes table. label Label start footnote. notation Notation footnotes (default = \"alphabet\"). See details.","code":""},{"path":"https://inkaverse.com/reference/footnotes.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Footnotes in tables — footnotes","text":"Table footnotes word html documents","code":""},{"path":"https://inkaverse.com/reference/footnotes.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Footnotes in tables — footnotes","text":"use pandoc format kable(format = \"pipe\"). can add footnote symbol using {hypen} table. notation use: \"alphabet\", \"number\", \"symbol\", \"none\".","code":""},{"path":"https://inkaverse.com/reference/gdoc2qmd.html","id":null,"dir":"Reference","previous_headings":"","what":"Google docs to Rmarkdown — gdoc2qmd","title":"Google docs to Rmarkdown — gdoc2qmd","text":"Use Articul8 Add-ons Google docs build Rticles","code":""},{"path":"https://inkaverse.com/reference/gdoc2qmd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Google docs to Rmarkdown — gdoc2qmd","text":"","code":"gdoc2qmd(file, export = NA)"},{"path":"https://inkaverse.com/reference/gdoc2qmd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Google docs to Rmarkdown — gdoc2qmd","text":"file Zip file path Articul8 exported md format [path] export Path export files [path: NA (file directory)]","code":""},{"path":"https://inkaverse.com/reference/gdoc2qmd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Google docs to Rmarkdown — gdoc2qmd","text":"path","code":""},{"path":"https://inkaverse.com/reference/gdoc2rmd.html","id":null,"dir":"Reference","previous_headings":"","what":"Google docs to Rmarkdown — gdoc2rmd","title":"Google docs to Rmarkdown — gdoc2rmd","text":"Use Articul8 Add-ons Google docs build Rticles","code":""},{"path":"https://inkaverse.com/reference/gdoc2rmd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Google docs to Rmarkdown — gdoc2rmd","text":"","code":"gdoc2rmd(file, export = \"files\", prefix_fig = \"Figure\", prefix_tab = \"Table\")"},{"path":"https://inkaverse.com/reference/gdoc2rmd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Google docs to Rmarkdown — gdoc2rmd","text":"file Zip file path Articul8 exported md format export path export files. Default file directory prefix_fig Prefix name figure prefix_tab Prefix name table","code":""},{"path":"https://inkaverse.com/reference/gdoc2rmd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Google docs to Rmarkdown — gdoc2rmd","text":"folder","code":""},{"path":"https://inkaverse.com/reference/H2cal.html","id":null,"dir":"Reference","previous_headings":"","what":"Broad-sense heritability in plant breeding — H2cal","title":"Broad-sense heritability in plant breeding — H2cal","text":"Heritability plant breeding genotype difference basis","code":""},{"path":"https://inkaverse.com/reference/H2cal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Broad-sense heritability in plant breeding — H2cal","text":"","code":"H2cal( data, trait, gen.name, rep.n, env.n = 1, year.n = 1, env.name = NULL, year.name = NULL, fixed.model, random.model, summary = FALSE, emmeans = FALSE, weights = NULL, plot_diag = FALSE, outliers.rm = FALSE, trial = NULL )"},{"path":"https://inkaverse.com/reference/H2cal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Broad-sense heritability in plant breeding — H2cal","text":"data Experimental design data frame factors traits. trait Name trait. gen.name Name genotypes. rep.n Number replications experiment. env.n Number environments (default = 1). See details. year.n Number years (default = 1). See details. env.name Name environments (default = NULL). See details. year.name Name years (default = NULL). See details. fixed.model fixed effects model (BLUEs). See examples. random.model random effects model (BLUPs). See examples. summary Print summary random model (default = FALSE). emmeans Use emmeans calculate BLUEs (default = FALSE). weights optional vector ‘prior weights’ used fitting process (default = NULL). plot_diag Show diagnostic plots fixed random effects (default = FALSE). Options: \"base\", \"ggplot\". . outliers.rm Remove outliers (default = FALSE). See references. trial Column name trial results (default = NULL).","code":""},{"path":"https://inkaverse.com/reference/H2cal.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Broad-sense heritability in plant breeding — H2cal","text":"list","code":""},{"path":"https://inkaverse.com/reference/H2cal.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Broad-sense heritability in plant breeding — H2cal","text":"function allows made calculation individual multi-environmental trials (MET) using fixed random model. 1. variance components based random model population summary information based fixed model (BLUEs). 2. Heritability three approaches: Standard (ANOVA), Cullis (BLUPs) Piepho (BLUEs). 3. Best Linear Unbiased Estimators (BLUEs), fixed effect. 4. Best Linear Unbiased Predictors (BLUPs), random effect. 5. Table outliers removed model. individual experiments necessary provide trait, gen.name, rep.n. MET experiments env.n env.name /year.n year.name according experiment. BLUEs calculation based pairwise comparison time consuming increase number genotypes. can specify emmeans = FALSE calculate BLUEs faster. emmeans = FALSE change 1 0 fixed model exclude intersect analysis get genotypes BLUEs. information review references.","code":""},{"path":"https://inkaverse.com/reference/H2cal.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Broad-sense heritability in plant breeding — H2cal","text":"Bernal Vasquez, Angela Maria, et al. “Outlier Detection Methods Generalized Lattices: Case Study Transition ANOVA REML.” Theoretical Applied Genetics, vol. 129, . 4, Apr. 2016. Buntaran, H., Piepho, H., Schmidt, P., Ryden, J., Halling, M., Forkman, J. (2020). Cross validation stagewise mixed model analysis Swedish variety trials winter wheat spring barley. Crop Science, 60(5). Schmidt, P., J. Hartung, J. Bennewitz, H.P. Piepho. 2019. Heritability Plant Breeding Genotype Difference Basis. Genetics 212(4). Schmidt, P., J. Hartung, J. Rath, H.P. Piepho. 2019. Estimating Broad Sense Heritability Unbalanced Data Agricultural Cultivar Trials. Crop Science 59(2). Tanaka, E., Hui, F. K. C. (2019). Symbolic Formulae Linear Mixed Models. H. Nguyen (Ed.), Statistics Data Science. Springer. Zystro, J., Colley, M., Dawson, J. (2018). Alternative Experimental Designs Plant Breeding. Plant Breeding Reviews. John Wiley Sons, Ltd.","code":""},{"path":"https://inkaverse.com/reference/H2cal.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Broad-sense heritability in plant breeding — H2cal","text":"Maria Belen Kistner Flavio Lozano Isla","code":""},{"path":"https://inkaverse.com/reference/H2cal.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Broad-sense heritability in plant breeding — H2cal","text":"","code":"library(inti) dt <- potato hr <- H2cal(data = dt , trait = \"stemdw\" , gen.name = \"geno\" , rep.n = 5 , fixed.model = \"0 + (1|bloque) + geno\" , random.model = \"1 + (1|bloque) + (1|geno)\" , emmeans = TRUE , plot_diag = FALSE , outliers.rm = TRUE ) hr$tabsmr #> trait rep geno env year mean std min max V.g V.e #> 1 stemdw 5 15 1 1 12.59867 4.749994 2.818 22.302 19.96002 9.410932 #> V.p repeatability H2.s H2.p H2.c #> 1 21.84221 0.913828 0.913828 0.9502395 0.9533473 hr$blues #> # A tibble: 15 × 6 #> geno stemdw SE df lower.CL upper.CL #> #> 1 G01 15.7 1.03 120. 13.7 17.8 #> 2 G02 10.1 1.03 120. 8.08 12.2 #> 3 G03 9.70 1.03 120. 7.65 11.7 #> 4 G04 15.2 1.03 120. 13.1 17.2 #> 5 G05 12.9 1.09 123. 10.7 15.0 #> 6 G06 22.3 1.03 120. 20.3 24.3 #> 7 G07 2.82 1.03 120. 0.778 4.86 #> 8 G08 10.4 1.03 120. 8.38 12.5 #> 9 G09 15.7 1.03 120. 13.6 17.7 #> 10 G10 9.24 1.03 120. 7.20 11.3 #> 11 G11 6.42 1.03 120. 4.38 8.47 #> 12 G12 16.1 1.03 120. 14.1 18.2 #> 13 G13 14.6 1.03 120. 12.6 16.7 #> 14 G14 16.3 1.03 120. 14.3 18.3 #> 15 G15 11.5 1.03 120. 9.43 13.5 hr$blups #> # A tibble: 15 × 2 #> geno stemdw #> #> 1 G01 15.6 #> 2 G02 10.2 #> 3 G03 9.82 #> 4 G04 15.1 #> 5 G05 12.8 #> 6 G06 20.6 #> 7 G07 3.25 #> 8 G08 10.5 #> 9 G09 15.5 #> 10 G10 9.39 #> 11 G11 6.70 #> 12 G12 15.9 #> 13 G13 14.5 #> 14 G14 16.1 #> 15 G15 11.5 hr$outliers #> $fixed #> bloque geno stemdw resi res_MAD rawp.BHStud index adjp bholm out_flag #> 68 IV G05 80.65 60.36709 18.84505 0 68 0 0 OUTLIER #> #> $random #> bloque geno stemdw resi res_MAD rawp.BHStud index adjp #> 68 IV G05 80.65 61.39925 18.886676 0.0000000000 68 0.0000000000 #> 100 IV G06 33.52 12.02340 3.698449 0.0002169207 100 0.0002169207 #> bholm out_flag #> 68 0.00000000 OUTLIER #> 100 0.03232119 OUTLIER #>"},{"path":"https://inkaverse.com/reference/include_figure.html","id":null,"dir":"Reference","previous_headings":"","what":"Figure with caption and notes — include_figure","title":"Figure with caption and notes — include_figure","text":"Include figures title notes using data base","code":""},{"path":"https://inkaverse.com/reference/include_figure.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Figure with caption and notes — include_figure","text":"","code":"include_figure(figure, caption = NA, notes = NA, label = NA)"},{"path":"https://inkaverse.com/reference/include_figure.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Figure with caption and notes — include_figure","text":"figure Path URL figure. caption Figure caption (default = NA). notes Figure notes (default = NA). label Label notes (default = NA).","code":""},{"path":"https://inkaverse.com/reference/include_figure.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Figure with caption and notes — include_figure","text":"Figure caption notes","code":""},{"path":"https://inkaverse.com/reference/include_figure.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Figure with caption and notes — include_figure","text":"","code":"library(inti) figure <- \"https://inkaverse.com/reference/figures/logo.png\" figure %>% include_figure(caption = \"Title test.\" , notes = \"Note test.\") #> $caption #> [1] \"Title test. Note test.\" #> #> $path #> [1] \"https://inkaverse.com/reference/figures/logo.png\" #> #> $figure #> [1] \"https://inkaverse.com/reference/figures/logo.png\" #> attr(,\"class\") #> [1] \"knit_image_paths\" \"knit_asis\" #>"},{"path":"https://inkaverse.com/reference/include_table.html","id":null,"dir":"Reference","previous_headings":"","what":"Table with footnotes — include_table","title":"Table with footnotes — include_table","text":"Include tables title footnotes word html documents","code":""},{"path":"https://inkaverse.com/reference/include_table.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Table with footnotes — include_table","text":"","code":"include_table(table, caption = NA, notes = NA, label = NA, notation = \"none\")"},{"path":"https://inkaverse.com/reference/include_table.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Table with footnotes — include_table","text":"table Data frame. caption Table caption (default = NULL). See details. notes Footnotes table (default = NA). See details. label Label start footnote (default = NA). notation Notation symbols footnotes (default = \"none\") Others: \"alphabet\", \"number\", \"symbol\".","code":""},{"path":"https://inkaverse.com/reference/include_table.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Table with footnotes — include_table","text":"Table caption footnotes","code":""},{"path":"https://inkaverse.com/reference/include_table.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Table with footnotes — include_table","text":"","code":"library(inti) table <- data.frame( x = rep_len(1, 5) , y = rep_len(3, 5) , z = rep_len(\"c\", 5) ) table %>% inti::include_table( caption = \"Title caption b) line 0 a) line 1 b) line 2\" , notes = \"Footnote\" , label = \"Where:\" ) #> #> #> Table: Title caption b) line 0 a) line 1 b) line 2 #> #> | x| y|z | #> |--:|--:|:--| #> | 1| 3|c | #> | 1| 3|c | #> | 1| 3|c | #> | 1| 3|c | #> | 1| 3|c | #> #> Where:<\/small> #> Footnote<\/small>"},{"path":"https://inkaverse.com/reference/jc_tombola.html","id":null,"dir":"Reference","previous_headings":"","what":"Journal Club Tombola — jc_tombola","title":"Journal Club Tombola — jc_tombola","text":"Function arrange journal club schedule","code":""},{"path":"https://inkaverse.com/reference/jc_tombola.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Journal Club Tombola — jc_tombola","text":"","code":"jc_tombola( data, members, papers = 1, group = NA, gr_lvl = NA, status = NA, st_lvl = \"active\", frq = 7, date = NA, seed = NA )"},{"path":"https://inkaverse.com/reference/jc_tombola.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Journal Club Tombola — jc_tombola","text":"data Data frame withe members information. members Columns members names. papers Number paper meeting group Column arrange group. gr_lvl Levels groups arrange. See details. status Column status members. st_lvl Level confirm assistance JC. See details. frq Number day session. date Date start first session JC. seed Number replicate results (default = date).","code":""},{"path":"https://inkaverse.com/reference/jc_tombola.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Journal Club Tombola — jc_tombola","text":"data frame schedule JC","code":""},{"path":"https://inkaverse.com/reference/jc_tombola.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Journal Club Tombola — jc_tombola","text":"function consider n levels gr_lvl. case levels using \"\" \"\" combination. suggested levels st_lvl : active spectator. \"active\" members enter schedule.","code":""},{"path":"https://inkaverse.com/reference/mean_comparison.html","id":null,"dir":"Reference","previous_headings":"","what":"Mean comparison test — mean_comparison","title":"Mean comparison test — mean_comparison","text":"Function compare treatment lm aov using data frames","code":""},{"path":"https://inkaverse.com/reference/mean_comparison.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Mean comparison test — mean_comparison","text":"","code":"mean_comparison( data, response, model_factors, comparison, test_comp = \"SNK\", sig_level = 0.05 )"},{"path":"https://inkaverse.com/reference/mean_comparison.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Mean comparison test — mean_comparison","text":"data Fieldbook data. response Model used experimental design. model_factors Factor model. comparison Significance level analysis (default = 0.05). test_comp Comparison test (default = \"SNK\"). Others: \"TUKEY\", \"DUNCAN\". sig_level Significance level analysis (default = 0.05).","code":""},{"path":"https://inkaverse.com/reference/mean_comparison.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Mean comparison test — mean_comparison","text":"list","code":""},{"path":"https://inkaverse.com/reference/mean_comparison.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Mean comparison test — mean_comparison","text":"","code":"if (FALSE) { library(inti) library(gsheet) url <- paste0(\"https://docs.google.com/spreadsheets/d/\" , \"15r7ZwcZZHbEgltlF6gSFvCTFA-CFzVBWwg3mFlRyKPs/\" , \"edit#gid=172957346\") # browseURL(url) fb <- gsheet2tbl(url) mc <- mean_comparison(data = fb , response = \"spad_29\" , model_factors = \"bloque* geno*treat\" , comparison = c(\"geno\", \"treat\") , test_comp = \"SNK\" ) mc$comparison mc$stat }"},{"path":"https://inkaverse.com/reference/met.html","id":null,"dir":"Reference","previous_headings":"","what":"Swedish cultivar trial data — met","title":"Swedish cultivar trial data — met","text":"datasets obtained official Swedish cultivar tests. Dry matter yield analyzed. trials laid alpha-designs two replicates. Within replicate, five seven incomplete blocks.","code":""},{"path":"https://inkaverse.com/reference/met.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Swedish cultivar trial data — met","text":"","code":"met"},{"path":"https://inkaverse.com/reference/met.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Swedish cultivar trial data — met","text":"data frame 1069 rows 8 variables: zone Sweden divided three different agricultural zones: South, Middle, North location Locations: 18 location Zones rep Replications (4): number replication experiment alpha Incomplete blocks (8) alpha-designs cultivar Cultivars (30): genotypes evaluated yield Yield kg/ha year Year (1): 2016 env enviroment (18): combination zone + location + year","code":""},{"path":"https://inkaverse.com/reference/met.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Swedish cultivar trial data — met","text":"doi:10.1002/csc2.20177","code":""},{"path":"https://inkaverse.com/reference/metamorphosis.html","id":null,"dir":"Reference","previous_headings":"","what":"Transform fieldbooks based in a dictionary — metamorphosis","title":"Transform fieldbooks based in a dictionary — metamorphosis","text":"Transform entire fieldbook according data dictionary","code":""},{"path":"https://inkaverse.com/reference/metamorphosis.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Transform fieldbooks based in a dictionary — metamorphosis","text":"","code":"metamorphosis(fieldbook, dictionary, from, to, index, colnames)"},{"path":"https://inkaverse.com/reference/metamorphosis.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Transform fieldbooks based in a dictionary — metamorphosis","text":"fieldbook Data frame original information. dictionary Data frame new names categories. See details. Column dictionary original names. Column dictionary new names. index Column dictionary type level variables. colnames Character vector name columns.","code":""},{"path":"https://inkaverse.com/reference/metamorphosis.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Transform fieldbooks based in a dictionary — metamorphosis","text":"List two objects. 1. New data frame. 2. Dictionary.","code":""},{"path":"https://inkaverse.com/reference/metamorphosis.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Transform fieldbooks based in a dictionary — metamorphosis","text":"function require least three columns. 1. Original names (). 2. New names (). 3. Variable type (index).","code":""},{"path":"https://inkaverse.com/reference/outliers_remove.html","id":null,"dir":"Reference","previous_headings":"","what":"Remove outliers — outliers_remove","title":"Remove outliers — outliers_remove","text":"Use method M4 Bernal Vasquez (2016). Bonferroni Holm test judge residuals standardized re scaled MAD (BH MADR).","code":""},{"path":"https://inkaverse.com/reference/outliers_remove.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Remove outliers — outliers_remove","text":"","code":"outliers_remove(data, trait, model)"},{"path":"https://inkaverse.com/reference/outliers_remove.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Remove outliers — outliers_remove","text":"data Experimental design data frame factors traits. trait Name trait. model fixed random effects model.","code":""},{"path":"https://inkaverse.com/reference/outliers_remove.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Remove outliers — outliers_remove","text":"list. 1. Table date without outliers. 2. outliers dataset.","code":""},{"path":"https://inkaverse.com/reference/outliers_remove.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Remove outliers — outliers_remove","text":"Function remove outliers MET experiments","code":""},{"path":"https://inkaverse.com/reference/outliers_remove.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Remove outliers — outliers_remove","text":"Bernal Vasquez, Angela Maria, et al. “Outlier Detection Methods Generalized Lattices: Case Study Transition ANOVA REML.” Theoretical Applied Genetics, vol. 129, . 4, Apr. 2016.","code":""},{"path":"https://inkaverse.com/reference/outliers_remove.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Remove outliers — outliers_remove","text":"","code":"library(inti) rmout <- outliers_remove( data = potato , trait =\"stemdw\" , model = \"0 + (1|bloque) + geno\" ) rmout$outliers #> bloque geno stemdw resi res_MAD rawp.BHStud index adjp bholm out_flag #> 68 IV G05 80.65 60.36709 18.84505 0 68 0 0 OUTLIER"},{"path":"https://inkaverse.com/reference/plot_diag.html","id":null,"dir":"Reference","previous_headings":"","what":"Diagnostic plots — plot_diag","title":"Diagnostic plots — plot_diag","text":"Function plot diagnostic models","code":""},{"path":"https://inkaverse.com/reference/plot_diag.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Diagnostic plots — plot_diag","text":"","code":"plot_diag(model, title = NA)"},{"path":"https://inkaverse.com/reference/plot_diag.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Diagnostic plots — plot_diag","text":"model Statistical model title Plot title","code":""},{"path":"https://inkaverse.com/reference/plot_diag.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Diagnostic plots — plot_diag","text":"plots","code":""},{"path":"https://inkaverse.com/reference/plot_diag.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Diagnostic plots — plot_diag","text":"","code":"if (FALSE) { dt <- potato lm <- aov(stemdw ~ bloque + geno*treat, dt) #lm <- lme4::lmer(stemdw ~ bloque + (1|geno:treat), dt) plot(lm, which = 1) plot_diag(lm)[3] plot(lm, which = 2) plot_diag(lm)[2] plot(lm, which = 3) plot_diag(lm)[4] plot(lm, which = 4) plot_diag(lm)[1] }"},{"path":"https://inkaverse.com/reference/plot_raw.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot raw data — plot_raw","title":"Plot raw data — plot_raw","text":"Function use raw data made boxplot graphic","code":""},{"path":"https://inkaverse.com/reference/plot_raw.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot raw data — plot_raw","text":"","code":"plot_raw( data, type = \"boxplot\", x, y, group = NULL, xlab = NULL, ylab = NULL, glab = NULL, ylimits = NULL, xlimits = NULL, xrotation = NULL, legend = \"top\", xtext = NULL, gtext = NULL, color = TRUE, linetype = 1, opt = NULL )"},{"path":"https://inkaverse.com/reference/plot_raw.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot raw data — plot_raw","text":"data raw data type Type graphic. \"boxplot\" \"scatterplot\" x Axis x variable y Axis y variable group Group variable xlab Title axis x ylab Title axis y glab Title legend ylimits Limits break y axis c(initial, end, brakes) xlimits scatter plot. Limits break x axis c(initial, end, brakes) xrotation Rotation x axis c(angle, h, v) legend position legends (\"none\", \"left\", \"right\", \"bottom\", \"top\", two-element numeric vector) xtext Text labels x axis using vector gtext Text labels groups using vector color Colored figure (TRUE), black & white (FALSE) color vector linetype Line type regression. Default = 0 opt Add new layers plot","code":""},{"path":"https://inkaverse.com/reference/plot_raw.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot raw data — plot_raw","text":"plot","code":""},{"path":"https://inkaverse.com/reference/plot_raw.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Plot raw data — plot_raw","text":"add additional layer plot using \"+\" ggplot2 options","code":""},{"path":"https://inkaverse.com/reference/plot_raw.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot raw data — plot_raw","text":"","code":"if (FALSE) { library(inti) fb <- potato fb %>% plot_raw(type = \"box\" , x = \"geno\" , y = \"twue\" , group = NULL , ylab = NULL , xlab = NULL , glab = \"\" ) fb %>% plot_raw(type = \"sca\" , x = \"geno\" , y = \"twue\" , group = \"treat\" , color = c(\"red\", \"blue\") ) }"},{"path":"https://inkaverse.com/reference/plot_smr.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot summary data — plot_smr","title":"Plot summary data — plot_smr","text":"Graph summary data bar o line plot","code":""},{"path":"https://inkaverse.com/reference/plot_smr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot summary data — plot_smr","text":"","code":"plot_smr( data, type = NULL, x = NULL, y = NULL, group = NULL, xlab = NULL, ylab = NULL, glab = NULL, ylimits = NULL, xrotation = c(0, 0.5, 0.5), xtext = NULL, gtext = NULL, legend = \"top\", sig = NULL, sigsize = 3, error = NULL, color = TRUE, opt = NULL )"},{"path":"https://inkaverse.com/reference/plot_smr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot summary data — plot_smr","text":"data Output summary data type Type graphic. \"bar\" \"line\" x Axis x variable y Axis y variable group Group variable xlab Title axis x ylab Title axis y glab Title legend ylimits limits y axis c(initial, end, brakes) xrotation Rotation x axis c(angle, h, v) xtext Text labels x axis using vector gtext Text labels group using vector legend position legends (\"none\", \"left\", \"right\", \"bottom\", \"top\", two-element numeric vector) sig Column significance sigsize Font size significance letters error Show error bar (\"ste\" \"std\") color colored figure (TRUE), black & white (FALSE) color vector opt Add news layer plot","code":""},{"path":"https://inkaverse.com/reference/plot_smr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot summary data — plot_smr","text":"plot","code":""},{"path":"https://inkaverse.com/reference/plot_smr.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Plot summary data — plot_smr","text":"table put mean_comparison(graph_opts = TRUE) function. contain parameter plot. add additional layer plot using \"+\" ggplot2 options","code":""},{"path":"https://inkaverse.com/reference/plot_smr.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot summary data — plot_smr","text":"","code":"if (FALSE) { library(inti) fb <- potato#' yrs <- yupana_analysis(data = fb , response = \"hi\" , model_factors = \"geno*treat\" , comparison = c(\"geno\", \"treat\") ) yrs$meancomp %>% plot_smr(type = \"line\" , x = \"geno\" , y = \"hi\" , xlab = \"\" , group = \"treat\" , glab = \"Tratamientos\" , ylimits = c(0, 1, 0.2) , color = c(\"red\", \"black\") , gtext = c(\"Irrigado\", \"Sequia\") ) }"},{"path":"https://inkaverse.com/reference/potato.html","id":null,"dir":"Reference","previous_headings":"","what":"Water use efficiency in 15 potato genotypes — potato","title":"Water use efficiency in 15 potato genotypes — potato","text":"Experiment evaluate physiological response 15 potatos genotypes water deficit condition. experiment randomized complete block design five replications. stress started 30 day planting.","code":""},{"path":"https://inkaverse.com/reference/potato.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Water use efficiency in 15 potato genotypes — potato","text":"","code":"potato"},{"path":"https://inkaverse.com/reference/potato.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Water use efficiency in 15 potato genotypes — potato","text":"data frame 150 rows 17 variables: treat Water deficit treatments: sequia, irrigado geno 15 potato genotypes bloque blocks experimentl design spad_29 Relative chlorophyll content (SPAD) 29 day planting spad_83 Relative chlorophyll content (SPAD) 84 day planting rwc_84 Relative water content (percentage) 84 day planting op_84 Osmotic potential (Mpa) 84 day planting leafdw leaf dry weight (g) stemdw stem dry weight (g) rootdw root dry weight (g) tubdw tuber dry weight (g) biomdw total biomass dry weight (g) hi harvest index ttrans total transpiration (l) wue water use effiency (g/l) twue tuber water use effiency (g/l) lfa leaf area (cm2)","code":""},{"path":"https://inkaverse.com/reference/reexports.html","id":null,"dir":"Reference","previous_headings":"","what":"Objects exported from other packages — reexports","title":"Objects exported from other packages — reexports","text":"objects imported packages. Follow links see documentation. dplyr %>%","code":""},{"path":"https://inkaverse.com/reference/split_folder.html","id":null,"dir":"Reference","previous_headings":"","what":"Split folder — split_folder","title":"Split folder — split_folder","text":"Function split folder size number elements","code":""},{"path":"https://inkaverse.com/reference/split_folder.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Split folder — split_folder","text":"","code":"split_folder( folder, export, units = \"megas\", size = 500, zip = TRUE, remove = FALSE )"},{"path":"https://inkaverse.com/reference/split_folder.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Split folder — split_folder","text":"folder Path folder split (path). export Path export split folders (path). units Units split folder (string: \"megas\", \"number\"). size Folder size units selected (numeric). zip Zip split folders (logical). remove Remove split folder zip (logical).","code":""},{"path":"https://inkaverse.com/reference/split_folder.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Split folder — split_folder","text":"zip files","code":""},{"path":"https://inkaverse.com/reference/split_folder.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Split folder — split_folder","text":"","code":"if (FALSE) { split_folder(\"pictures/QUINOA 2018-2019 SC SEEDS EDWIN - CAMACANI/\" , \"pictures/split_num\", remove = T, size = 400, units = \"number\") }"},{"path":"https://inkaverse.com/reference/table2qmd.html","id":null,"dir":"Reference","previous_headings":"","what":"Table to Rmarkdown — table2qmd","title":"Table to Rmarkdown — table2qmd","text":"Use Articul8 Add-ons Google docs build Rticles","code":""},{"path":"https://inkaverse.com/reference/table2qmd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Table to Rmarkdown — table2qmd","text":"","code":"table2qmd(text)"},{"path":"https://inkaverse.com/reference/table2qmd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Table to Rmarkdown — table2qmd","text":"text Markdown text table information (string)","code":""},{"path":"https://inkaverse.com/reference/table2qmd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Table to Rmarkdown — table2qmd","text":"string mutated","code":""},{"path":"https://inkaverse.com/reference/table2rmd.html","id":null,"dir":"Reference","previous_headings":"","what":"Table to Rmarkdown — table2rmd","title":"Table to Rmarkdown — table2rmd","text":"Use Articul8 Add-ons Google docs build Rticles","code":""},{"path":"https://inkaverse.com/reference/table2rmd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Table to Rmarkdown — table2rmd","text":"","code":"table2rmd(text, opts = NA, prefix = \"Table\")"},{"path":"https://inkaverse.com/reference/table2rmd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Table to Rmarkdown — table2rmd","text":"text String table information opts chunk options brackets. prefix Prefix name table","code":""},{"path":"https://inkaverse.com/reference/table2rmd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Table to Rmarkdown — table2rmd","text":"Mutated string","code":""},{"path":"https://inkaverse.com/reference/tarpuy.html","id":null,"dir":"Reference","previous_headings":"","what":"Interactive fieldbook designs — tarpuy","title":"Interactive fieldbook designs — tarpuy","text":"Invoke RStudio addin create fieldbook designs","code":""},{"path":"https://inkaverse.com/reference/tarpuy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Interactive fieldbook designs — tarpuy","text":"","code":"tarpuy(dependencies = FALSE)"},{"path":"https://inkaverse.com/reference/tarpuy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Interactive fieldbook designs — tarpuy","text":"dependencies Install package dependencies run app","code":""},{"path":"https://inkaverse.com/reference/tarpuy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Interactive fieldbook designs — tarpuy","text":"Shiny app","code":""},{"path":"https://inkaverse.com/reference/tarpuy.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Interactive fieldbook designs — tarpuy","text":"Tarpuy allow create experimental designs interactive app.","code":""},{"path":"https://inkaverse.com/reference/tarpuy.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Interactive fieldbook designs — tarpuy","text":"","code":"if(interactive()){ inti::tarpuy() }"},{"path":"https://inkaverse.com/reference/tarpuy_design.html","id":null,"dir":"Reference","previous_headings":"","what":"Fieldbook experimental designs — tarpuy_design","title":"Fieldbook experimental designs — tarpuy_design","text":"Function deploy experimental designs","code":""},{"path":"https://inkaverse.com/reference/tarpuy_design.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fieldbook experimental designs — tarpuy_design","text":"","code":"tarpuy_design( data, nfactors = 1, type = \"crd\", rep = 2, serie = 2, seed = 0, barcode = NA )"},{"path":"https://inkaverse.com/reference/tarpuy_design.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fieldbook experimental designs — tarpuy_design","text":"data Experimental design data frame factors level. See examples. nfactors Number factor experiment(default = 1). See details. type Type experimental arrange (default = \"crd\"). See details. rep Number replications experiment (default = 3). serie Digits plot id (default = 2). seed Replicability draw results (default = 0) always random. See details. barcode Barcode prefix data collection.","code":""},{"path":"https://inkaverse.com/reference/tarpuy_design.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fieldbook experimental designs — tarpuy_design","text":"list fieldbook design","code":""},{"path":"https://inkaverse.com/reference/tarpuy_design.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Fieldbook experimental designs — tarpuy_design","text":"function allows include arguments sheet information design. include 2 columns sheet: {arguments} {values}. See examples. information extracted automatically deploy design. nfactors = 1: crd, rcbd, lsd, lattice. nfactors = 2 (factorial): split-crd, split-rcbd split-lsd nfactors >= 2 (factorial): crd, rcbd, lsd.","code":""},{"path":"https://inkaverse.com/reference/tarpuy_design.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fieldbook experimental designs — tarpuy_design","text":"","code":"if (FALSE) { library(inti) library(gsheet) url <- paste0(\"https://docs.google.com/spreadsheets/d/\" , \"1tDcLZZ9C80uBJK8RRW6_WIyxiL8bIHACStJyf8avuCU/edit#gid=59220990\") # browseURL(url) fb <- gsheet2tbl(url) tarpuy_design(data = fb) }"},{"path":"https://inkaverse.com/reference/tarpuy_plex.html","id":null,"dir":"Reference","previous_headings":"","what":"Fieldbook plan information — tarpuy_plex","title":"Fieldbook plan information — tarpuy_plex","text":"Information build plan experiment (PLEX)","code":""},{"path":"https://inkaverse.com/reference/tarpuy_plex.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fieldbook plan information — tarpuy_plex","text":"","code":"tarpuy_plex( data = NULL, idea = NULL, goal = NULL, hypothesis = NULL, rationale = NULL, objectives = NULL, plan = NULL, institutions = NULL, researchers = NULL, manager = NULL, location = NULL, altitude = NULL, georeferencing = NULL, environment = NULL, start = NA, end = NA, about = NULL, fieldbook = NULL, album = NULL, github = NULL, nfactor = 2, design = \"rcbd\", rep = 3, serie = 2, seed = 0 )"},{"path":"https://inkaverse.com/reference/tarpuy_plex.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fieldbook plan information — tarpuy_plex","text":"data Data fieldbook information. idea idea born. goal main goal project. hypothesis expected results. rationale Based evidence planned experiment. objectives objectives project. plan General description project (M & M). institutions Institutions involved project. researchers Persons involved project. manager Persons responsible collection data. location Location project. altitude Altitude experiment (m..s.l). georeferencing Georeferencing information. environment Environment experiment (greenhouse, lab, etc). start date start experiments. end date end experiments. Short description project. fieldbook Name ID fieldbook/project. album link photos project. github link github repository. nfactor Number factors design. design Type design. rep Number replication. serie Number digits plots. seed Seed randomization.","code":""},{"path":"https://inkaverse.com/reference/tarpuy_plex.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fieldbook plan information — tarpuy_plex","text":"data frame list arguments: info variables design logbook timetable budget","code":""},{"path":"https://inkaverse.com/reference/tarpuy_plex.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Fieldbook plan information — tarpuy_plex","text":"Provide information available.","code":""},{"path":"https://inkaverse.com/reference/tarpuy_plotdesign.html","id":null,"dir":"Reference","previous_headings":"","what":"Fieldbook plot experimental designs — tarpuy_plotdesign","title":"Fieldbook plot experimental designs — tarpuy_plotdesign","text":"Plot fieldbook sketch designs based experimental design","code":""},{"path":"https://inkaverse.com/reference/tarpuy_plotdesign.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fieldbook plot experimental designs — tarpuy_plotdesign","text":"","code":"tarpuy_plotdesign( data, factor, dim = NULL, fill = \"plots\", xlab = NULL, ylab = NULL, glab = NULL )"},{"path":"https://inkaverse.com/reference/tarpuy_plotdesign.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fieldbook plot experimental designs — tarpuy_plotdesign","text":"data Experimental design data frame factors level. See examples. factor Vector name columns factors. dim Dimension reshape design arrangement. fill Value fill experimental units (default = \"plots\"). xlab Title x axis. ylab Title y axis. glab Title group axis.","code":""},{"path":"https://inkaverse.com/reference/tarpuy_plotdesign.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fieldbook plot experimental designs — tarpuy_plotdesign","text":"plot","code":""},{"path":"https://inkaverse.com/reference/tarpuy_plotdesign.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Fieldbook plot experimental designs — tarpuy_plotdesign","text":"function allows plot experimental design according field experiment design.","code":""},{"path":"https://inkaverse.com/reference/tarpuy_varlist.html","id":null,"dir":"Reference","previous_headings":"","what":"Fieldbook variable list — tarpuy_varlist","title":"Fieldbook variable list — tarpuy_varlist","text":"Function include variables evaluate fieldbook design.","code":""},{"path":"https://inkaverse.com/reference/tarpuy_varlist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fieldbook variable list — tarpuy_varlist","text":"","code":"tarpuy_varlist(fieldbook, varlist = NULL)"},{"path":"https://inkaverse.com/reference/tarpuy_varlist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fieldbook variable list — tarpuy_varlist","text":"fieldbook Data frame fieldbook. varlist Data frame variables information. See examples.","code":""},{"path":"https://inkaverse.com/reference/tarpuy_varlist.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fieldbook variable list — tarpuy_varlist","text":"data frame","code":""},{"path":"https://inkaverse.com/reference/tarpuy_varlist.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Fieldbook variable list — tarpuy_varlist","text":"function allows include arguments sheet information variables. include 3 columns sheet: {abbreviation}, {evaluation} {sampling}. See examples. information extracted automatically deploy list variable fieldbook design.","code":""},{"path":"https://inkaverse.com/reference/tarpuy_varlist.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fieldbook variable list — tarpuy_varlist","text":"","code":"if (FALSE) { library(inti) library(gsheet) url <- paste0(\"https://docs.google.com/spreadsheets/d/\" , \"183upHd4wriZz2UnInoo5Ate5YFdk7cZlhE0sMQ2x5iw/edit#gid=532773890\") # browseURL(url) info <- gsheet2tbl(url) fieldbook <- tarpuy_design(data = info) url_var <- paste0(\"https://docs.google.com/spreadsheets/d/\" , \"183upHd4wriZz2UnInoo5Ate5YFdk7cZlhE0sMQ2x5iw/edit#gid=1335288687\") varlist <- gsheet2tbl(url_var) tarpuy_varlist(fieldbook = fieldbook, varlist = varlist) }"},{"path":"https://inkaverse.com/reference/web_table.html","id":null,"dir":"Reference","previous_headings":"","what":"HTML tables for markdown documents — web_table","title":"HTML tables for markdown documents — web_table","text":"Export tables download, pasta copy buttons","code":""},{"path":"https://inkaverse.com/reference/web_table.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"HTML tables for markdown documents — web_table","text":"","code":"web_table( data, caption = NULL, digits = 2, rnames = FALSE, buttons = NULL, file_name = \"file\", scrolly = NULL, columnwidth = \"200px\", width = \"100%\" )"},{"path":"https://inkaverse.com/reference/web_table.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"HTML tables for markdown documents — web_table","text":"data Dataset. caption Title table. digits Digits number table exported. rnames Row names. buttons Buttons: \"excel\", \"copy\" \"none\". Default c(\"excel\", \"copy\") file_name Excel file name scrolly Windows height show table. Default \"45vh\" columnwidth Column width. Default '200px' width Width pixels percentage (Defaults automatic sizing)","code":""},{"path":"https://inkaverse.com/reference/web_table.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"HTML tables for markdown documents — web_table","text":"table markdown format html documents","code":""},{"path":"https://inkaverse.com/reference/web_table.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"HTML tables for markdown documents — web_table","text":"","code":"if (FALSE) { library(inti) met %>% web_table(caption = \"Web table\") }"},{"path":"https://inkaverse.com/reference/yupana.html","id":null,"dir":"Reference","previous_headings":"","what":"Interactive data analysis — yupana","title":"Interactive data analysis — yupana","text":"Invoke RStudio addin analyze graph experimental design data","code":""},{"path":"https://inkaverse.com/reference/yupana.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Interactive data analysis — yupana","text":"","code":"yupana(dependencies = FALSE)"},{"path":"https://inkaverse.com/reference/yupana.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Interactive data analysis — yupana","text":"dependencies Install package dependencies run app","code":""},{"path":"https://inkaverse.com/reference/yupana.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Interactive data analysis — yupana","text":"Shiny app","code":""},{"path":"https://inkaverse.com/reference/yupana.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Interactive data analysis — yupana","text":"Yupana: data analysis graphics experimental designs.","code":""},{"path":"https://inkaverse.com/reference/yupana.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Interactive data analysis — yupana","text":"","code":"if(interactive()){ inti::yupana() }"},{"path":"https://inkaverse.com/reference/yupana_analysis.html","id":null,"dir":"Reference","previous_headings":"","what":"Fieldbook analysis report — yupana_analysis","title":"Fieldbook analysis report — yupana_analysis","text":"Function create complete report fieldbook","code":""},{"path":"https://inkaverse.com/reference/yupana_analysis.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fieldbook analysis report — yupana_analysis","text":"","code":"yupana_analysis( data, last_factor = NULL, response, model_factors, comparison, test_comp = \"SNK\", sig_level = 0.05, plot_dist = \"boxplot\", plot_diag = FALSE, digits = 2 )"},{"path":"https://inkaverse.com/reference/yupana_analysis.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fieldbook analysis report — yupana_analysis","text":"data Field book data. last_factor last factor fieldbook. response Response variable. model_factors Model used experimental design. comparison Factors compare test_comp Comprasison test c(\"SNK\", \"TUKEY\", \"DUNCAN\") sig_level Significal test (default: p = 0.005) plot_dist Plot data distribution (default = \"boxplot\") plot_diag Diagnostic plots model (default = FALSE). digits Digits number table exported.","code":""},{"path":"https://inkaverse.com/reference/yupana_analysis.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fieldbook analysis report — yupana_analysis","text":"list","code":""},{"path":"https://inkaverse.com/reference/yupana_analysis.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fieldbook analysis report — yupana_analysis","text":"","code":"if (FALSE) { library(inti) fb <- potato rsl <- yupana_analysis(data = fb , last_factor = \"bloque\" , response = \"spad_83\" , model_factors = \"geno * treat\" , comparison = c(\"geno\", \"treat\") ) }"},{"path":"https://inkaverse.com/reference/yupana_export.html","id":null,"dir":"Reference","previous_headings":"","what":"Graph options to export — yupana_export","title":"Graph options to export — yupana_export","text":"Function export graph options model parameters","code":""},{"path":"https://inkaverse.com/reference/yupana_export.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Graph options to export — yupana_export","text":"","code":"yupana_export( data, type = NA, xlab = NA, ylab = NA, glab = NA, ylimits = NA, xrotation = c(0, 0.5, 0.5), xtext = NA, gtext = NA, legend = \"top\", sig = NA, error = NA, color = TRUE, opt = NA, dimension = c(20, 10, 100) )"},{"path":"https://inkaverse.com/reference/yupana_export.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Graph options to export — yupana_export","text":"data Result yupana_analysis yupana_import. type Plot type xlab Title axis x ylab Title axis y glab Title legend ylimits limits y axis xrotation Rotation x axis c(angle, h, v) xtext Text labels x axis gtext Text labels group legend position legends (\"none\", \"left\", \"right\", \"bottom\", \"top\", two-element numeric vector) sig Column significance error Show error bar (\"ste\" \"std\"). color colored figure (TRUE), otherwise black & white (FALSE) opt Add news layer plot dimension Dimension graphs","code":""},{"path":"https://inkaverse.com/reference/yupana_export.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Graph options to export — yupana_export","text":"data frame","code":""},{"path":"https://inkaverse.com/reference/yupana_export.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Graph options to export — yupana_export","text":"","code":"if (FALSE) { library(inti) library(gsheet) url <- paste0(\"https://docs.google.com/spreadsheets/d/\" , \"15r7ZwcZZHbEgltlF6gSFvCTFA-CFzVBWwg3mFlRyKPs/edit#gid=172957346\") # browseURL(url) fb <- gsheet2tbl(url) smr <- yupana_analysis(data = fb , last_factor = \"bloque\" , response = \"spad_83\" , model_factors = \"block + geno*treat\" , comparison = c(\"geno\", \"treat\") ) gtab <- yupana_export(smr, type = \"line\", ylimits = c(0, 100, 2)) #> import url <- paste0(\"https://docs.google.com/spreadsheets/d/\" , \"15r7ZwcZZHbEgltlF6gSFvCTFA-CFzVBWwg3mFlRyKPs/edit#gid=1202800640\") # browseURL(url) fb <- gsheet2tbl(url) info <- yupana_import(fb) etab <- yupana_export(info) info2 <- yupana_import(etab) etab2 <- yupana_export(info2) }"},{"path":"https://inkaverse.com/reference/yupana_import.html","id":null,"dir":"Reference","previous_headings":"","what":"Import information from data summary — yupana_import","title":"Import information from data summary — yupana_import","text":"Graph summary data","code":""},{"path":"https://inkaverse.com/reference/yupana_import.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Import information from data summary — yupana_import","text":"","code":"yupana_import(data)"},{"path":"https://inkaverse.com/reference/yupana_import.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Import information from data summary — yupana_import","text":"data Summary information options","code":""},{"path":"https://inkaverse.com/reference/yupana_import.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Import information from data summary — yupana_import","text":"list","code":""},{"path":"https://inkaverse.com/reference/yupana_import.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Import information from data summary — yupana_import","text":"","code":"if (FALSE) { library(inti) library(gsheet) url <- paste0(\"https://docs.google.com/spreadsheets/d/\" , \"15r7ZwcZZHbEgltlF6gSFvCTFA-CFzVBWwg3mFlRyKPs/edit#gid=338518609\") # browseURL(url) fb <- gsheet2tbl(url) info <- yupana_import(fb) }"},{"path":"https://inkaverse.com/reference/yupana_mvr.html","id":null,"dir":"Reference","previous_headings":"","what":"Multivariate Analysis — yupana_mvr","title":"Multivariate Analysis — yupana_mvr","text":"Multivariate analysis PCA HCPC","code":""},{"path":"https://inkaverse.com/reference/yupana_mvr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multivariate Analysis — yupana_mvr","text":"","code":"yupana_mvr( data, last_factor = NULL, summary_by = NULL, groups = NULL, variables = NULL )"},{"path":"https://inkaverse.com/reference/yupana_mvr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Multivariate Analysis — yupana_mvr","text":"data Field book data. last_factor last factor fieldbook. summary_by Variables group analysis. groups Groups color PCA. variables Variables use analysis.","code":""},{"path":"https://inkaverse.com/reference/yupana_mvr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Multivariate Analysis — yupana_mvr","text":"result plots","code":""},{"path":"https://inkaverse.com/reference/yupana_mvr.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multivariate Analysis — yupana_mvr","text":"Compute plot information multivariate analysis (PCA, HCPC correlation).","code":""},{"path":"https://inkaverse.com/reference/yupana_mvr.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multivariate Analysis — yupana_mvr","text":"","code":"if (FALSE) { library(inti) library(gsheet) url <- paste0(\"https://docs.google.com/spreadsheets/d/\" , \"15r7ZwcZZHbEgltlF6gSFvCTFA-CFzVBWwg3mFlRyKPs/edit#gid=172957346\") # browseURL(url) fb <- gsheet2tbl(url) mv <- yupana_mvr(data = fb , last_factor = \"bloque\" , summary_by = c(\"geno\", \"treat\") , groups = NULL ) FactoMineR::plot.PCA(mv$pca, choix = \"ind\", habillage = mv$param$groups) }"},{"path":"https://inkaverse.com/reference/yupana_reshape.html","id":null,"dir":"Reference","previous_headings":"","what":"Fieldbook reshape — yupana_reshape","title":"Fieldbook reshape — yupana_reshape","text":"Function reshape fieldbook according separation character","code":""},{"path":"https://inkaverse.com/reference/yupana_reshape.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fieldbook reshape — yupana_reshape","text":"","code":"yupana_reshape( data, last_factor, sep, new_colname, from_var = NULL, to_var = NULL, exc_factors = NULL )"},{"path":"https://inkaverse.com/reference/yupana_reshape.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fieldbook reshape — yupana_reshape","text":"data Field book raw data. last_factor last factor field book. sep Character separates last value. new_colname new name column created. from_var first variable case want exclude several. variables. to_var last variable case want exclude several variables. exc_factors Factor exclude reshape.","code":""},{"path":"https://inkaverse.com/reference/yupana_reshape.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fieldbook reshape — yupana_reshape","text":"data frame","code":""},{"path":"https://inkaverse.com/reference/yupana_reshape.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Fieldbook reshape — yupana_reshape","text":"variable name variable_evaluation_rep. reshape function help create column rep new variable name variable_evaluation.","code":""},{"path":"https://inkaverse.com/news/index.html","id":"inti-057","dir":"Changelog","previous_headings":"","what":"inti 0.5.7","title":"inti 0.5.7","text":"figure2rmd() ==> figure2qmd() table2rmd() ==> table2qmd() gdocs2rmd() ==> gdocs2qmd() Fix plot_raw(): “length(x) = 2 > 1’ coercion ’logical(1)” Update jc_tombola()","code":""},{"path":"https://inkaverse.com/news/index.html","id":"inti-056","dir":"Changelog","previous_headings":"","what":"inti 0.5.6","title":"inti 0.5.6","text":"CRAN release: 2022-05-19 autoWidth = TRUE columnwidth argument width argument Fix plot_smr(): “length(x) = 2 > 1’ coercion ’logical(1)” New function: split_folder() Yupana: add scale method correlation plot","code":""},{"path":"https://inkaverse.com/news/index.html","id":"inti-055","dir":"Changelog","previous_headings":"","what":"inti 0.5.5","title":"inti 0.5.5","text":"CRAN release: 2022-04-01 Yupana: update yupana_import() using if_any() instead across() Tarpuy: dsg column qr ==> barcode Tarpuy: update sheets names intro section Tarypu: export field-book specific sheet Tarpuy: select sheet field-book sketch Tarpuy: create field-book factor list Tarpuy: column [] design omitted field-book generation CRAN comments: (class(model) == “lmerMod”) => ( (model, “lmerMod”)","code":""},{"path":"https://inkaverse.com/news/index.html","id":"inti-054","dir":"Changelog","previous_headings":"","what":"inti 0.5.4","title":"inti 0.5.4","text":"CRAN release: 2022-02-22 outliers_remove(): change cbind() cbind.data.frame(). Fix apps auth. Thanks Uwe Ligges allow consecutive CRAN updates.","code":""},{"path":"https://inkaverse.com/news/index.html","id":"inti-053","dir":"Changelog","previous_headings":"","what":"inti 0.5.3","title":"inti 0.5.3","text":"CRAN release: 2022-02-18 Complete location name experimental information. Avoid labels axis legend using \"\". Update vignettes using bookdown. Fix table summary H2cal(). Update diagnostic plot plot_diag() lm lmerMod. Update code logIn modules apps. Update correlation graph yupana.","code":""},{"path":"https://inkaverse.com/news/index.html","id":"inti-052","dir":"Changelog","previous_headings":"","what":"inti 0.5.2","title":"inti 0.5.2","text":"CRAN release: 2021-12-19 Fix CRAN comments Fix path install Tarpuy dependencies Include huito logo apps Fix factors Tarpuy field-book export Update code tarpuy_design() Update barcode column split using “_” Update function tarpuy_plex()","code":""},{"path":"https://inkaverse.com/news/index.html","id":"inti-051","dir":"Changelog","previous_headings":"","what":"inti 0.5.1","title":"inti 0.5.1","text":"CRAN release: 2021-12-10 Thanks Jim Holland (@ncsumaize) suggestion improve function. Use Articul8 Add-ons Google docs build Rticles Update pkgdown","code":""},{"path":"https://inkaverse.com/news/index.html","id":"inti-050","dir":"Changelog","previous_headings":"","what":"inti 0.5.0","title":"inti 0.5.0","text":"CRAN release: 2021-11-07 Changes incompatible old versions. Extract information yupana_analysis Import information web yupana_analysis Update function H2cal() Include statistics anova table export results Clean headers export data, exclude “{}” Update load/save interface can exclude: {evaluation} {sampling}","code":""},{"path":"https://inkaverse.com/news/index.html","id":"inti-044","dir":"Changelog","previous_headings":"","what":"inti 0.4.4","title":"inti 0.4.4","text":"CRAN release: 2021-10-01 Update function selection paper meeting Include last_factor selection Function need last_factor Include package version apps Fixed navigation bar apps PCA individual bottom Include version output table Dimension plots multivariate analysis","code":""},{"path":"https://inkaverse.com/news/index.html","id":"inti-043","dir":"Changelog","previous_headings":"","what":"inti 0.4.3","title":"inti 0.4.3","text":"CRAN release: 2021-09-08 Show equation adjusted R scatter plot graph sig include variables summary table plots number reps 1 sig error “none”","code":""},{"path":"https://inkaverse.com/news/index.html","id":"inti-042","dir":"Changelog","previous_headings":"","what":"inti 0.4.2","title":"inti 0.4.2","text":"CRAN release: 2021-08-15 Include info plot_smr() plot_raw Delete legend border Transparent logos background New vignette coding yupana Update Rticles Books template Fix web_table() export xlsx plot_raw() scientific notation labels Include new data set potato Legend position load correct Headers [] excluded analysis Agradecimiento Pedro Barriga por sus sugerencias para mejorar yupana()","code":""},{"path":"https://inkaverse.com/news/index.html","id":"inti-041","dir":"Changelog","previous_headings":"","what":"inti 0.4.1","title":"inti 0.4.1","text":"CRAN release: 2021-06-25 Add significance font size Allows vector colors plots Include “scatter plot” H2cal() include trial option MET New video version > 0.4.1 Add equations regressions plot Include scatter plot “Exploratory” module","code":""},{"path":"https://inkaverse.com/news/index.html","id":"inti-040","dir":"Changelog","previous_headings":"","what":"inti 0.4.0","title":"inti 0.4.0","text":"CRAN release: 2021-05-25 Changes incompatible old versions.","code":""},{"path":"https://inkaverse.com/news/index.html","id":"major-changes-0-4-0","dir":"Changelog","previous_headings":"","what":"Major changes","title":"inti 0.4.0","text":"Deprecated: create_rticles() & rticles() Deprecated shiny app: rticles Rticles Books Vignette explain dependencies use rticles Styled messages New module: Exploratory need fbsm Reactivity analysis Export model information Overwrite graph info Design 3 factor use facet_grid() Allow import/export information plots Reduce font size significance Styled messages Vignette explain modules app Overwrite fieldbook info Box plot graph Can used independently Table create footnotes rename functions Include new logo Vignettes: comparison H2cal() asreml Add data base MET Logo package apps Agradecimiento Khaterine por la idea en el diseño de los logos","code":""},{"path":"https://inkaverse.com/news/index.html","id":"inti-030","dir":"Changelog","previous_headings":"","what":"inti 0.3.0","title":"inti 0.3.0","text":"CRAN release: 2021-04-24 Fix {arguments} xlimits ylimits Update tables style Update template files Vignette describe arguments options Yupana Delete redundant functions info_figure() & info_grahics() Update functions: include_figure() & include_figure() xtext: labels x level gtext: labels group levels","code":""},{"path":"https://inkaverse.com/news/index.html","id":"inti-020","dir":"Changelog","previous_headings":"","what":"inti 0.2.0","title":"inti 0.2.0","text":"CRAN release: 2021-04-14 Changes incompatible old versions.","code":""},{"path":"https://inkaverse.com/news/index.html","id":"major-changes-0-2-0","dir":"Changelog","previous_headings":"","what":"Major changes","title":"inti 0.2.0","text":"Arguments changed syntax fbsm graphics. Delete error messages console run app Change dependency: ggpubr –> cowplot Multivariate analysis need factor levels n>2 Allows copy Statistics table Delete error messages console run app fix dates experiments update code unzip Articul8 files remove treatments column Allows plot 3 factors comparison facet_grid() New arguments plot: xlimits, xrotation, dimension, opt Delete redundant arguments; limits, brakes Suggest use “*” instead “:” Include additional layers plot. e.g. coord_flip() Save plot dimensions exported sheet web_table fix resize table web","code":""},{"path":"https://inkaverse.com/news/index.html","id":"bug-fixes-0-2-0","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"inti 0.2.0","text":"add pkgs.R file load dependencies apps fix auto-install packages inti::tarpuy(T)","code":""},{"path":"https://inkaverse.com/news/index.html","id":"inti-013","dir":"Changelog","previous_headings":"","what":"inti 0.1.3","title":"inti 0.1.3","text":"CRAN release: 2021-03-20","code":""},{"path":"https://inkaverse.com/news/index.html","id":"major-changes-0-1-3","dir":"Changelog","previous_headings":"","what":"Major changes","title":"inti 0.1.3","text":"update bootstrap include code section google auth verification Include QR code fieldbook bslib dependence install CRAN Include video local installation Suppress messages load apps","code":""},{"path":"https://inkaverse.com/news/index.html","id":"inti-012","dir":"Changelog","previous_headings":"","what":"inti 0.1.2","title":"inti 0.1.2","text":"CRAN release: 2020-11-25","code":""},{"path":"https://inkaverse.com/news/index.html","id":"major-changes-0-1-2","dir":"Changelog","previous_headings":"","what":"Major changes","title":"inti 0.1.2","text":"Exclude package multtest depends CRAN error: include_table Search engine web page","code":""},{"path":"https://inkaverse.com/news/index.html","id":"inti-011","dir":"Changelog","previous_headings":"","what":"inti 0.1.1","title":"inti 0.1.1","text":"CRAN release: 2020-11-17","code":""},{"path":"https://inkaverse.com/news/index.html","id":"major-changes-0-1-1","dir":"Changelog","previous_headings":"","what":"Major changes","title":"inti 0.1.1","text":"now apps work locally update bootstrap update packages dependencies apps Graphs: button generate refresh graphs Fieldbook: plot_label fieldbook summary label axis plots Analysis: export analysis sheet name Analysis: round digits export table new functions: info_figure() & info_table() update pkgdown documentation","code":""},{"path":"https://inkaverse.com/news/index.html","id":"bug-fixes-0-1-1","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"inti 0.1.1","text":"fix problem ‘cloud.json’ Multivariate: exclude variables without variation PCA Multivariate: exclude columns NA values Graphs: app stop graph arguments wrong update observeEvent() –> reactive() update app new bookdown release","code":""},{"path":"https://inkaverse.com/news/index.html","id":"inti-010","dir":"Changelog","previous_headings":"","what":"inti 0.1.0","title":"inti 0.1.0","text":"CRAN release: 2020-10-22 First package release","code":""}] +[{"path":"https://inkaverse.com/articles/apps.html","id":"install-the-apps-locally","dir":"Articles","previous_headings":"","what":"Install the apps locally","title":"Apps","text":"case need change email account o renew credentials access apps can use googlesheets4::gs4_token().","code":""},{"path":"https://inkaverse.com/articles/apps.html","id":"tarpuy","dir":"Articles","previous_headings":"","what":"Tarpuy","title":"Apps","text":"Ease way deploy field-book experimental plans. demo options Tarpuy","code":""},{"path":"https://inkaverse.com/articles/apps.html","id":"yupana","dir":"Articles","previous_headings":"","what":"Yupana","title":"Apps","text":"Data analysis graphics experimental designs. demo options Yupana","code":""},{"path":"https://inkaverse.com/articles/apps.html","id":"huito","dir":"Articles","previous_headings":"","what":"Huito","title":"Apps","text":"open-source R package deploys flexible reproducible labels using layers. Huito Project","code":""},{"path":"https://inkaverse.com/articles/apps.html","id":"germinar-germinaquant","dir":"Articles","previous_headings":"","what":"GerminaR + GerminaQuant","title":"Apps","text":"GerminaR first platform base open source package calculate graphic germination indices R. GerminaR include web application called “GerminQuant R” non programming users. GerminaR Demo GerminaQuant Project","code":""},{"path":"https://inkaverse.com/articles/apps.html","id":"citation","dir":"Articles","previous_headings":"GerminaR + GerminaQuant","what":"Citation","title":"Apps","text":"Lozano-Isla, Flavio; Benites-Alfaro, Omar Eduardo; Pompelli, Marcelo Francisco (2019). GerminaR: R package germination analysis interactive web application “GerminaQuant R.” Ecological Research, 34(2), 339–346. https://doi.org/10.1111/1440-1703.1275","code":""},{"path":"https://inkaverse.com/articles/extra/stagewise.html","id":null,"dir":"Articles > Extra","previous_headings":"","what":"Load data","title":"Stagewise mixed-model analysis","text":"","code":"library(inti) library(purrr) library(dplyr) fb <- inti::met"},{"path":[]},{"path":"https://inkaverse.com/articles/extra/stagewise.html","id":null,"dir":"Articles > Extra","previous_headings":"","what":"asreml","title":"Stagewise mixed-model analysis","text":"","code":"library(asreml) library(data.table) library(plyr) library(stringr) asreml.options(maxit=100) # Set asreml iteration ############################ ##### Stage I LSMEANS ##### ##### per location ##### ww <- data.table(fb) ##### Make column Zone_Loc ##### trials <- nlevels(ww$env) envs <- levels(ww$env) ##### Make data list for Stage I ##### data_list <- matrix(data=list(), nrow=length(envs), ncol=1, dimnames=list(envs, c(\"data_Set\"))) ##### Make a list of Trials ##### for(i in 1:trials){ print(i) b <- levels(ww$env) c <- b[i] env <- as.factor(c) env <- data.table(env) f <- merge(ww,env,by=\"env\") assign(paste0(\"data_\", b[i]), f) data_list[[i, \"data_Set\" ]] <- f rm(b, c, f, env) } data_list <- data.table(ldply(data_list[, \"data_Set\"], data.frame, .id=\"env\")) stgI_list <- matrix(data=list(), nrow=length(envs), ncol=1, dimnames=list(envs, c(\"lsmeans\"))) asreml.options(maxit=100) # Set asreml iteration ############################ ##### Stage I LSMEANS ##### ##### per location ##### for (i in envs){ edat <- droplevels(subset(ww, env == i)) print(i) mod.1 <- asreml(fixed = yield ~ cultivar, random = ~ rep + rep:alpha, data = edat, predict = predict.asreml(classify = \"cultivar\")) update.asreml(mod.1) print(summary.asreml(mod.1)$varcomp) blue <- predict(mod.1, classify=\"cultivar\", levels=levels(edat$cultivar), vcov=TRUE,aliased = T) # get the lsmeans blue.1 <- data.table(blue$pvals)[, c(1:3)] names(blue.1) <- c(\"cultivar\", \"yield_lsm\", \"se\") blue.1[ , ':='(var=se^2, smith.w=diag(solve(blue$vcov)))] # calculate the Smith's weight stgI_list[[i, \"lsmeans\" ]] <- blue.1 # put all the results of Stage 1 in the list rm(Edat,mod.1, blue, blue.1) } ####################################################### ##### Preparing dataset of Stage I for Stage II ###### ##### Unlist the results of Stage I and format as data.table ##### stgII_list <- data.table(plyr::ldply(stgI_list[, \"lsmeans\"], data.frame, .id=\"env\")) stgII_list$zone<- factor(str_split_fixed(stgII_list$env, \"_\", 2)[,1]) # Make Zone column by split the record in Zone_Loc column stgII_list$location <- factor(str_split_fixed(stgII_list$env, \"_\", 3)[,2]) # Make Location by split the record in Zone_Loc column stgII_list$year <- factor(str_split_fixed(stgII_list$env, \"_\", 3)[,3]) # Make Year by split the record in Zone_Loc column blues.asreml <- stgII_list ############################ ##### Stage II BLUPs ###### ##### Zone analysis ##### model <- asreml(yield_lsm ~ zone, random = ~cultivar + zone:location + zone:cultivar + cultivar:zone:location, weights = smith.w, family = asr_gaussian(dispersion=1.0), # fix residual variance to 1 data = blues.asreml, predict = predict.asreml(classify = \"cultivar\") ) update.asreml(model) # print(summary.asreml(model)$varcomp) # print the variance components blups.asrml <- data.frame((model$predictions$pvals[1:4]))"},{"path":"https://inkaverse.com/articles/extra/stagewise.html","id":null,"dir":"Articles > Extra","previous_headings":"","what":"H2cal","title":"Stagewise mixed-model analysis","text":"Fixed model 0 + avoid intercep calculate BLUEs. emmeans = F calculate Smith weitghts first stage","code":"library(inti) library(purrr) #> First stage envs <- levels(fb$env) model <- 1:length(envs) %>% map(function(x) { model <- fb %>% filter(env %in% envs[x]) %>% H2cal(trait = \"yield\" , gen.name = \"cultivar\" , rep.n = 4 , fixed.model = \"0 + (1|rep) + (1|rep:alpha) + cultivar\" , random.model = \"1 + (1|rep) + (1|rep:alpha) + (1|cultivar)\" # , plot_diag = T , emmeans = F ) blues <- model$blues %>% mutate(trial = levels(fb$env)[x]) }) blues.h2cal <- bind_rows(model) %>% separate(trial, c(\"zone\", \"location\", \"year\")) %>% mutate(across(c(yield, smith.w), as.numeric)) %>% mutate(across(!c(yield, smith.w), as.factor)) #> Second stage met <- blues.h2cal %>% mutate(across(!yield, as.factor)) %>% H2cal(trait = \"yield\" , gen.name = \"cultivar\" , rep.n = 4 , env.n = 18 , env.name = \"location\" , fixed.model = \"0 + zone + (1|zone:location) + (1|zone:cultivar) + cultivar\" , random.model = \"1 + zone + (1|zone:location) + (1|zone:cultivar) + (1|cultivar)\" # , plot_diag = T , emmeans = T # , weights = blues.h2cal$smith.w ) blups.h2cal <- met$blups"},{"path":"https://inkaverse.com/articles/extra/stagewise.html","id":null,"dir":"Articles > Extra","previous_headings":"","what":"BLUEs comparison","title":"Stagewise mixed-model analysis","text":"BLUEs correlation H2Cal asrml (r = 1)","code":"blues.comp <- merge(blues.asreml , blues.h2cal , by = c(\"cultivar\", \"zone\", \"location\")) # plot(blues.comp$yield, blues.comp$yield_lsm) rs <- cor(blues.comp$yield, blues.comp$yield_lsm) cat(\"r =\", rs) ## r = 1 blues.comp %>% web_table(digits = 4)"},{"path":"https://inkaverse.com/articles/extra/stagewise.html","id":null,"dir":"Articles > Extra","previous_headings":"","what":"BLUPs comparison","title":"Stagewise mixed-model analysis","text":"BLUPs correlation H2Cal asrml (r = 0.9818724)","code":"blups.comp <- merge(blups.asrml, blups.h2cal , by = c(\"cultivar\")) # plot(blups.comp$yield, blups.comp$predicted.value) rs <- cor(blups.comp$yield, blups.comp$predicted.value) cat(\"r =\", rs) ## r = 0.9818724 blups.comp %>% web_table(digits = 4)"},{"path":[]},{"path":"https://inkaverse.com/articles/extra/stagewise.html","id":null,"dir":"Articles > Extra","previous_headings":"","what":"asreml","title":"Stagewise mixed-model analysis","text":"","code":"library(asreml) options(\"scipen\"=100,\"digits\"= 4 ) asreml.options(maxit=100) # Set asreml iteration ##### Fit a single-stage model ##### ## incomplete block and replicate location-specific ## location-specifice residual variance mod <- asreml(fixed = yield ~ zone, random = ~ rep:at(location) + rep:alpha:at(location) + zone:location + cultivar + cultivar:zone:location+ cultivar:zone, residual = ~ dsum(~(units)|location), data = fb, predict = predict.asreml(classify = \"cultivar\")) update.asreml(mod) blups.asreml <- data.frame((mod$predictions$pvals[1:4]))"},{"path":"https://inkaverse.com/articles/extra/stagewise.html","id":null,"dir":"Articles > Extra","previous_headings":"","what":"H2cal","title":"Stagewise mixed-model analysis","text":"","code":"library(inti) model <- fb %>% H2cal(trait = \"yield\" , gen.name = \"cultivar\" , env.name = \"location\" , rep.n = 2 , env.n = 18 , fixed.model = \"0 + zone + (1|rep:location) + (1|rep:alpha:location) + (1|zone:location) + (1|cultivar:zone) + (1|cultivar:zone:location) + cultivar\" , random.model = \"1 + zone + (1|rep:location) + (1|rep:alpha:location) + (1|zone:location) + (1|cultivar:zone) + (1|cultivar:zone:location) + (1|cultivar)\" , summary = T , emmeans = T # , plot_diag = T ) ## Linear mixed model fit by REML ['lmerMod'] ## Formula: yield ~ 1 + zone + (1 | rep:location) + (1 | rep:alpha:location) + ## (1 | zone:location) + (1 | cultivar:zone) + (1 | cultivar:zone:location) + ## (1 | cultivar) ## Data: dt.rm ## Weights: weights ## ## REML criterion at convergence: 11933.2 ## ## Scaled residuals: ## Min 1Q Median 3Q Max ## -5.0760 -0.3308 -0.0084 0.3698 4.0576 ## ## Random effects: ## Groups Name Variance Std.Dev. ## cultivar:zone:location (Intercept) 2209.5 47.00 ## rep:alpha:location (Intercept) 944.6 30.73 ## cultivar:zone (Intercept) 129.6 11.38 ## rep:location (Intercept) 334.0 18.28 ## cultivar (Intercept) 728.0 26.98 ## zone:location (Intercept) 48679.8 220.64 ## Residual 1396.8 37.37 ## Number of obs: 1069, groups: ## cultivar:zone:location, 539; rep:alpha:location, 251; cultivar:zone, 90; rep:location, 36; cultivar, 30; zone:location, 18 ## ## Fixed effects: ## Estimate Std. Error t value ## (Intercept) 813.35 83.85 9.700 ## zonenorth 51.57 129.67 0.398 ## zonesouth 59.75 123.21 0.485 ## ## Correlation of Fixed Effects: ## (Intr) znnrth ## zonenorth -0.644 ## zonesouth -0.678 0.439 blups.h2cal <- model$blups"},{"path":"https://inkaverse.com/articles/extra/stagewise.html","id":null,"dir":"Articles > Extra","previous_headings":"","what":"BLUPs comparison","title":"Stagewise mixed-model analysis","text":"BLUPs correlation H2Cal asrml (r = 0.9201732)","code":"blups.comp <- merge(blups.asreml, blups.h2cal, by = c(\"cultivar\")) # plot(blups.comp$predicted.value, blups.comp$yield) rs <- cor(blups.comp$predicted.value, blups.comp$yield) cat(\"r =\", rs) ## r = 0.9201732 blups.comp %>% web_table(digits = 4)"},{"path":[]},{"path":"https://inkaverse.com/articles/extra/yupana-coding.html","id":"packages","dir":"Articles > Extra","previous_headings":"","what":"Packages","title":"Yupana: coding workflow","text":"","code":"library(inti) library(gsheet) library(FactoMineR) library(cowplot) library(png)"},{"path":"https://inkaverse.com/articles/extra/yupana-coding.html","id":"import-data","dir":"Articles > Extra","previous_headings":"","what":"Import data","title":"Yupana: coding workflow","text":"","code":"url <- paste0(\"https://docs.google.com/spreadsheets/d/\" , \"15r7ZwcZZHbEgltlF6gSFvCTFA-CFzVBWwg3mFlRyKPs/edit#gid=172957346\") # browseURL(url) fb <- url %>% gsheet2tbl() %>% rename_with(tolower) %>% mutate(across(c(treat, geno, bloque), ~ as.factor(.))) %>% mutate(across(where(is.factor), ~ gsub(\"[[:space:]]\", \"\", .)) ) %>% as.data.frame() # str(fb)"},{"path":[]},{"path":"https://inkaverse.com/articles/extra/yupana-coding.html","id":"box-plot","dir":"Articles > Extra","previous_headings":"Plot raw data","what":"Box plot","title":"Yupana: coding workflow","text":"","code":"wue <- fb %>% plot_raw(type = \"boxplot\" , x = \"geno\" , y = \"wue\" , group = \"treat\" , xlab = \"Genotipos\" , ylab = \"Water use efficiency (g/l)\" , ylimits = c(5, 30, 5) , glab = \"Tratamientos\" )"},{"path":"https://inkaverse.com/articles/extra/yupana-coding.html","id":"scatter-plot","dir":"Articles > Extra","previous_headings":"Plot raw data","what":"Scatter plot","title":"Yupana: coding workflow","text":"","code":"hi <- fb %>% plot_raw(type = \"scatterplot\" , x = \"hi\" , y = \"twue\" , group = \"treat\" , xlab = \"Harvest Index\" , ylab = \"Tuber water use efficiency (g/l)\" , glab = \"Tratamientos\" )"},{"path":"https://inkaverse.com/articles/extra/yupana-coding.html","id":"plot-in-grids","dir":"Articles > Extra","previous_headings":"Plot raw data","what":"Plot in grids","title":"Yupana: coding workflow","text":"Water use effiency 15 potato genotypes : ) Box plot. B) Scatter plot","code":"grid <- plot_grid(wue, hi , nrow = 2 , labels = \"AUTO\") save_plot(\"files/fig-01.png\" , plot = grid , dpi= 300 , base_width = 10 , base_height = 10 , scale = 1.4 , units = \"cm\" ) fig <- include_figure( figure = \"files/fig-01.png\" , caption = \"Water use effiency in 15 potato genotypes\" , notes = \" A) Box plot. B) Scatter plot \" , label = \"__Where:__\" ) fig$figure"},{"path":[]},{"path":"https://inkaverse.com/articles/extra/yupana-coding.html","id":"leaf-area","dir":"Articles > Extra","previous_headings":"Plot summary data","what":"Leaf area","title":"Yupana: coding workflow","text":"","code":"#> Plot summary data model <- fb %>% yupana_analysis(response = \"lfa\" , model_factors = \"geno*treat\" , comparison = c(\"geno\", \"treat\") ) lfa <- model$meancomp %>% plot_smr(type = \"bar\" , x = \"geno\" , y = \"lfa\" , group = \"treat\" , ylimits = c(0, 12000, 2000) , sig = \"sig\" , error = \"ste\" , xlab = \"Genotipos\" , ylab = \"Area foliar (cm^2)\" , color = F ) model$anova %>% anova() ## Analysis of Variance Table ## ## Response: lfa ## Df Sum Sq Mean Sq F value Pr(>F) ## geno 14 261742780 18695913 33.371 < 0.00000000000000022 *** ## treat 1 788562704 788562704 1407.541 < 0.00000000000000022 *** ## geno:treat 14 108153220 7725230 13.789 < 0.00000000000000022 *** ## Residuals 120 67228987 560242 ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 model$meancomp %>% web_table()"},{"path":"https://inkaverse.com/articles/extra/yupana-coding.html","id":"tuber-water-use-efficiency","dir":"Articles > Extra","previous_headings":"Plot summary data","what":"Tuber water use efficiency","title":"Yupana: coding workflow","text":"","code":"model <- fb %>% yupana_analysis(response = \"twue\" , model_factors = \"block + geno*treat\" , comparison = c(\"geno\", \"treat\") ) twue <- model$meancomp %>% plot_smr(type = \"line\" , x = \"geno\" , y = \"twue\" , group = \"treat\" , ylimits = c(0, 10, 2) , error = \"ste\" , color = c(\"blue\", \"red\") , ) + labs(x = \"Genotipos\" , y = \"Tuber water use effiency (g/l)\" ) model$anova %>% anova() ## Analysis of Variance Table ## ## Response: twue ## Df Sum Sq Mean Sq F value Pr(>F) ## block 1 20.78 20.7770 31.0214 0.0000001609 *** ## geno 14 413.06 29.5046 44.0523 < 0.00000000000000022 *** ## treat 1 2.04 2.0370 3.0414 0.08375 . ## geno:treat 14 16.07 1.1479 1.7140 0.06138 . ## Residuals 119 79.70 0.6698 ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 model$meancomp %>% web_table()"},{"path":"https://inkaverse.com/articles/extra/yupana-coding.html","id":"plot-in-grids-1","dir":"Articles > Extra","previous_headings":"Plot summary data","what":"Plot in grids","title":"Yupana: coding workflow","text":"Water use effiency 15 potato genotypes : ) Bar plot B) Line plot","code":"grid <- plot_grid(lfa, twue , nrow = 2 , labels = \"AUTO\") ggsave2(\"files/fig-02.png\" , plot = grid , dpi= 300 , width = 10 , height = 10 , scale = 1.5 , units = \"cm\") fig <- include_figure( figure = \"files/fig-02.png\" , caption = \"Water use effiency in 15 potato genotypes\" , notes = \" A) Bar plot B) Line plot \" , label = \"__Where:__\" ) fig$figure"},{"path":"https://inkaverse.com/articles/extra/yupana-coding.html","id":"multivariate-analysis","dir":"Articles > Extra","previous_headings":"","what":"Multivariate analysis","title":"Yupana: coding workflow","text":"Multivariate Analysis: Principal component analysis hierarchical clustering analysis.","code":"#> Principal component Analysis mv <- fb %>% yupana_mvr(last_factor = \"bloque\" , summary_by = c(\"geno\", \"treat\") , groups = \"treat\" ) # sink(\"files/pca.txt\") # # Results # summary(pca, nbelements = Inf, nb.dec = 2) # # Correlation de dimensions # dimdesc(pca) # sink() ppi <- 300 png(\"files/plot_pca_var.png\", width=7*ppi, height=7*ppi, res=ppi) plot.PCA(mv$pca, choix=\"var\", title=\"\", autoLab = \"y\", cex = 0.8, shadowtext = T) graphics.off() ppi <- 300 png(\"files/plot_pca_ind.png\", width=7*ppi, height=7*ppi, res=ppi) plot.PCA(mv$pca, choix=\"ind\", habillage = 2, title=\"\", autoLab = \"y\", cex = 0.8, shadowtext = T, label = \"ind\", legend = list(bty = \"y\", x = \"topright\")) graphics.off() # Hierarchical Clustering Analysis clt <- mv$pca %>% HCPC(., nb.clust=-1, graph = F) # sink(\"files/clu.txt\") # clus$call$t$tree # clus$desc.ind # clus$desc.var # sink() ppi <- 300 png(\"files/plot_cluster_tree.png\", width=7*ppi, height=7*ppi, res=ppi) plot.HCPC(x = clt, choice = \"tree\") graphics.off() ppi <- 300 png(\"files/plot_cluster_map.png\", width=7*ppi, height=7*ppi, res=ppi) plot.HCPC(x = clt, choice = \"map\") graphics.off() plot.01 <- readPNG(\"files/plot_pca_var.png\") %>% grid::rasterGrob() plot.02 <- readPNG(\"files/plot_pca_ind.png\") %>% grid::rasterGrob() plot.03 <- readPNG(\"files/plot_cluster_map.png\") %>% grid::rasterGrob() plot.04 <- readPNG(\"files/plot_cluster_tree.png\") %>% grid::rasterGrob() plot <- plot_grid(plot.01, plot.02, plot.03, plot.04 , nrow = 2 , labels = \"AUTO\") ggsave2(\"files/fig-03.png\" , plot = plot , dpi = 300 , width = 12 , height = 10 , scale = 1.5 , units = \"cm\") fig <- include_figure( caption = \"Multivariate Analysis: Principal component analysis and hierarchical clustering analysis.\" , figure = \"files/fig-03.png\" ) fig$figure"},{"path":"https://inkaverse.com/articles/heritability.html","id":"broad-sense-heritability-h2","dir":"Articles","previous_headings":"","what":"Broad-sense heritability (\\(H^2\\))","title":"Broad-sense heritability in plant breeding","text":"Broad-sense heritability (\\(H^2\\)) defined proportion phenotypic variance attributable overall genetic variance genotype (Schmidt et al., 2019b). usually additional interpretations associated \\(H^2\\): () equivalent coefficient determination linear regression unobservable genotypic value observed phenotype; (ii) also squared correlation predicted phenotypic value genotypic value; (iii) represents proportion selection differential (\\(S\\)) can realized response selection (\\(R\\)) (Falconer Mackay, 2005). two main reasons heritability entry-mean basis interest plant breeding (Schmidt et al., 2019a): plugged breeder’s Equation predict response selection. descriptive measure used assess usefulness precision results cultivar evaluation trials.","code":""},{"path":"https://inkaverse.com/articles/heritability.html","id":"breeders-equation","dir":"Articles","previous_headings":"Broad-sense heritability (\\(H^2\\))","what":"Breeder´s equation","title":"Broad-sense heritability in plant breeding","text":"\\[\\Delta G=H^2S\\] : \\(\\Delta G\\) genetic gain \\(S\\) mean phenotypic value selected genotypes, expressed deviation population mean.","code":""},{"path":"https://inkaverse.com/articles/heritability.html","id":"usual-problems","dir":"Articles","previous_headings":"","what":"Usual Problems","title":"Broad-sense heritability in plant breeding","text":"practice, trials conducted multienvironment trial (MET) presente unbalanced data cultivars tested environment simply plot data lost number replicates location varies genotypes (Schmidt et al., 2019b). However, standard method estimating heritability implicitly assumes balanced data, independent genotype effects, homogeneous variances.","code":""},{"path":"https://inkaverse.com/articles/heritability.html","id":"how-calculate-the-heritability","dir":"Articles","previous_headings":"","what":"How calculate the Heritability?","title":"Broad-sense heritability in plant breeding","text":"According Schmidt et al. (2019a), variance components calculated two ways:","code":""},{"path":"https://inkaverse.com/articles/heritability.html","id":"two-stages-approach","dir":"Articles","previous_headings":"How calculate the Heritability?","what":"1) Two stages approach","title":"Broad-sense heritability in plant breeding","text":"two stage approach, first stage experiment analyzed individually according experiment design (Lattice, CRBD, etc) (Zystro et al., 2018). second stage environments denotes year--location interaction. approach assumes single variance genotype--environment interactions (GxE), even multiple locations tested across multiple years (Buntaran et al., 2020).","code":""},{"path":"https://inkaverse.com/articles/heritability.html","id":"model","dir":"Articles","previous_headings":"How calculate the Heritability? > 1) Two stages approach","what":"Model","title":"Broad-sense heritability in plant breeding","text":"\\[y_{ikt}=\\mu\\ +\\ G_i+E_t+GxE_{}+\\varepsilon_{ikt}\\]","code":""},{"path":"https://inkaverse.com/articles/heritability.html","id":"phenotypic-variance","dir":"Articles","previous_headings":"How calculate the Heritability? > 1) Two stages approach","what":"Phenotypic variance","title":"Broad-sense heritability in plant breeding","text":"\\[\\sigma_p^2=\\sigma_g^2+\\frac{\\sigma_{g\\cdot e}^2}{n_e}+\\frac{\\sigma_{\\varepsilon}^2}{n_e\\cdot n_r}\\]","code":""},{"path":"https://inkaverse.com/articles/heritability.html","id":"one-stage-approach","dir":"Articles","previous_headings":"How calculate the Heritability?","what":"2) One stage approach","title":"Broad-sense heritability in plant breeding","text":"one stage approach one model used MET analysis. environmental effects included via separate year, location main interaction effects. \\[y_{ikt}=\\mu+G_i+Y_m+E_q+YxE_{mq}+GxY_{im}+GxE_{iq}+GxYxE_{imq}+\\varepsilon_{ikmq}\\]","code":""},{"path":"https://inkaverse.com/articles/heritability.html","id":"phenotypic-variance-1","dir":"Articles","previous_headings":"How calculate the Heritability? > 2) One stage approach","what":"Phenotypic variance","title":"Broad-sense heritability in plant breeding","text":"\\[\\sigma_p^2=\\sigma_g^2+\\frac{\\sigma_{g\\cdot e}^2}{n_e}+\\frac{\\sigma_{g\\cdot y}^2}{n_y}+\\frac{\\sigma_{g\\cdot y\\cdot e}^2}{n_y\\cdot n_e}+\\ \\frac{\\sigma_{\\epsilon}^2}{n_e\\cdot n_y\\cdot n_r}\\]","code":""},{"path":"https://inkaverse.com/articles/heritability.html","id":"differentes-heritability-calculations","dir":"Articles","previous_headings":"","what":"Differentes heritability calculations","title":"Broad-sense heritability in plant breeding","text":"Table 1: Differentes heritability calculation","code":""},{"path":"https://inkaverse.com/articles/heritability.html","id":"heritability-function-in-the-package","dir":"Articles","previous_headings":"","what":"Heritability function in the package","title":"Broad-sense heritability in plant breeding","text":"calculate standard heritability MET experiments number location replication include manually function H2cal(). case difference number replication experiments, take maximum value (often done practice) (Schmidt et al., 2019b). remove outliers function implemented Method 4 used Bernal-Vasquez et al. (2016): Bonferroni-Holm using re-scaled MAD standardizing residuals (BH-MADR).","code":""},{"path":"https://inkaverse.com/articles/heritability.html","id":"load-packages","dir":"Articles","previous_headings":"Heritability function in the package","what":"Load packages","title":"Broad-sense heritability in plant breeding","text":"","code":"library(inti)"},{"path":"https://inkaverse.com/articles/heritability.html","id":"h2cal-function","dir":"Articles","previous_headings":"Heritability function in the package","what":"H2cal function","title":"Broad-sense heritability in plant breeding","text":"","code":"dt <- potato hr <- H2cal(data = dt , trait = \"stemdw\" , gen.name = \"geno\" , rep.n = 5 , fixed.model = \"0 + (1|bloque) + geno\" , random.model = \"1 + (1|bloque) + (1|geno)\" , emmeans = TRUE , plot_diag = TRUE , outliers.rm = TRUE )"},{"path":"https://inkaverse.com/articles/heritability.html","id":"model-information","dir":"Articles","previous_headings":"Heritability function in the package","what":"Model information","title":"Broad-sense heritability in plant breeding","text":"","code":"hr$model %>% summary() ## Linear mixed model fit by REML ['lmerMod'] ## Formula: stemdw ~ 1 + (1 | bloque) + (1 | geno) ## Data: dt.rm ## Weights: weights ## ## REML criterion at convergence: 796.1 ## ## Scaled residuals: ## Min 1Q Median 3Q Max ## -2.38440 -0.64247 -0.08589 0.57452 2.84508 ## ## Random effects: ## Groups Name Variance Std.Dev. ## geno (Intercept) 19.960 4.4677 ## bloque (Intercept) 0.110 0.3316 ## Residual 9.411 3.0677 ## Number of obs: 148, groups: geno, 15; bloque, 5 ## ## Fixed effects: ## Estimate Std. Error t value ## (Intercept) 12.51 1.19 10.51"},{"path":"https://inkaverse.com/articles/heritability.html","id":"variance-components","dir":"Articles","previous_headings":"Heritability function in the package","what":"Variance components","title":"Broad-sense heritability in plant breeding","text":"Table 2: Variance component table","code":"hr$tabsmr %>% kable(caption = \"Variance component table\")"},{"path":"https://inkaverse.com/articles/heritability.html","id":"best-linear-unbiased-estimators-blues","dir":"Articles","previous_headings":"Heritability function in the package","what":"Best Linear Unbiased Estimators (BLUEs)","title":"Broad-sense heritability in plant breeding","text":"Table 3: BLUEs","code":"hr$blues %>% kable(caption = \"BLUEs\")"},{"path":"https://inkaverse.com/articles/heritability.html","id":"best-linear-unbiased-predictors-blups","dir":"Articles","previous_headings":"Heritability function in the package","what":"Best Linear Unbiased Predictors (BLUPs)","title":"Broad-sense heritability in plant breeding","text":"Table 4: BLUPs","code":"hr$blups %>% kable(caption = \"BLUPs\")"},{"path":"https://inkaverse.com/articles/heritability.html","id":"outliers","dir":"Articles","previous_headings":"Heritability function in the package","what":"Outliers","title":"Broad-sense heritability in plant breeding","text":"Table 5: Outliers fixed model Table 6: Outliers random model","code":"hr$outliers$fixed %>% kable(caption = \"Outliers fixed model\") hr$outliers$random %>% kable(caption = \"Outliers random model\")"},{"path":"https://inkaverse.com/articles/heritability.html","id":"comparison-h2cal-and-asreml","dir":"Articles","previous_headings":"","what":"Comparison: H2cal and asreml","title":"Broad-sense heritability in plant breeding","text":"https://inkaverse.com/articles/extra/stagewise.html","code":""},{"path":"https://inkaverse.com/articles/policy.html","id":"privacy-policy-for-apps-that-access-google-apis","dir":"Articles","previous_headings":"","what":"Privacy policy for apps that access Google APIs","title":"Inkaverse Privacy Policy","text":"Inkaverse maintains several web apps make easier work Google APIs R: Yupana wraps Sheets API Tarpuy wraps Sheets API apps governed common policies recorded . apps use internal resources owned “inkaverse” project Google Cloud Platform. name see consent screen. Exception: gmailr use resources owned inkaverse Package, due special requirements around Gmail scopes. use Google APIs apps subject API’s respective terms service. See https://developers.google.com/terms/.","code":""},{"path":[]},{"path":[]},{"path":"https://inkaverse.com/articles/policy.html","id":"accessing-user-data","dir":"Articles","previous_headings":"Privacy > Google account and user data","what":"Accessing user data","title":"Inkaverse Privacy Policy","text":"applications access Google resources local machine web. machine communicates directly Google APIs. inkaverse API Packages project never receives data permission access data. owners project can see anonymous, aggregated information usage tokens obtained OAuth client, APIs endpoints used. package includes functions can execute order read modify data. can happen provide token, requires authenticate specific Google user authorize actions. package can help get token guiding OAuth flow browser. must consent allow inkaverse API Packages operate behalf. OAuth consent screen describe scope authorized, e.g., name target API(s) whether authorizing “read ” “read write” access. two ways use apps without authorizing inkaverse API Packages: bring service account token configure package use OAuth client choice.","code":""},{"path":"https://inkaverse.com/articles/policy.html","id":"scopes","dir":"Articles","previous_headings":"Privacy > Google account and user data","what":"Scopes","title":"Inkaverse Privacy Policy","text":"Overview scopes requested various inkaverse API Packages rationale: Sheets (read/write): googlesheets4 package used apps allows manage spreadsheets therefore default scopes include read/write access. googlesheets4 package makes possible get token limited scope, e.g. read .","code":""},{"path":"https://inkaverse.com/articles/policy.html","id":"sharing-user-data","dir":"Articles","previous_headings":"Privacy > Google account and user data","what":"Sharing user data","title":"Inkaverse Privacy Policy","text":"package communicate Google APIs. user data shared owners inkaverse API Package servers.","code":""},{"path":"https://inkaverse.com/articles/policy.html","id":"storing-user-data","dir":"Articles","previous_headings":"Privacy > Google account and user data","what":"Storing user data","title":"Inkaverse Privacy Policy","text":"package may store credentials local machine, later reuse . Use caution using packages shared machine. default, OAuth token cached local file, ~/.R/gargle/gargle-oauth. See documentation gargle::gargle_options() gargle::credentials_user_oauth2() information control location token cache suppress token caching, globally individual token level.","code":""},{"path":"https://inkaverse.com/articles/policy.html","id":"policies-for-authors-of-packages-or-other-applications","dir":"Articles","previous_headings":"","what":"Policies for authors of packages or other applications","title":"Inkaverse Privacy Policy","text":"use API key client ID inkaverse API Packages external package tool. Per Google User Data Policy https://developers.google.com/terms/api-services-user-data-policy, application must accurately represent authenticating Google API services. use inkaverse package inside another package application executes logic — opposed code inkaverse API Packages user — must communicate clearly user. use credentials inkaverse API Package; instead, use credentials associated project user.","code":""},{"path":"https://inkaverse.com/articles/rticles.html","id":"herramientas-para-documentos-reproducibles","dir":"Articles","previous_headings":"","what":"Herramientas para documentos reproducibles","title":"Rticles","text":"Para la construcción de documentos técnico/científicos con R, deben crearse algunas cuentas e instalar los programas que necesitamos. Estas herramientas son independientes del sistema operativo, de acceso libre y pueden ser usadas para la investigación reproducible. El listado de herramientas son una recomendación basada en mi experiencia, y son las únicas herramientas.","code":""},{"path":"https://inkaverse.com/articles/rticles.html","id":"cuentas","dir":"Articles","previous_headings":"Herramientas para documentos reproducibles","what":"Cuentas","title":"Rticles","text":"Se recomienda usar el mismo correo para todas las cuentas. El uso de correos diferentes para cada servicio dificultará el flujo de trabajo. Deben crearse una cuenta en los siguientes servicios: Google (Gmail). Se recomienda que tengan una cuenta de Google ya que nos permitirá tener acceso Gsuit que posee un conjunto de herramientas gratuitas en línea. Estas herramientas son un buen complemento para el trabajo en equipo y puedes acceder ellos desde distintos dispositivos móviles. Zotero. Será nuestra biblioteca virtual, y una de las herramientas que más usaremos, ya que nos permitirá organizar nuestro trabajo y citar los documentos en nuestros manuscritos. GitHub. Es un servicio de repositorio de código. Nos ayudará organizar nuestros proyectos y códigos. Nos permite visualizar los historiales de cambio de nuestro proyecto, compartir nuestro código y generar páginas webs para publicar documentos en línea.","code":""},{"path":[]},{"path":"https://inkaverse.com/articles/rticles.html","id":"programas","dir":"Articles","previous_headings":"Herramientas para documentos reproducibles","what":"Programas","title":"Rticles","text":"Instalar los siguientes programas en el orden que se mencionan, para evitar conflictos en su funcionamiento. Zotero. Es un gestor de referencias bibliográficas, libre, abierto y gratuito desarrollado por el Center History New Media de la Universidad George Mason. R CRAN. Es un entorno de lenguaje de programación con un enfoque al análisis estadístico. El software R viene por defecto con funcionalidades básicas y para ampliar estas debemos instalar paquetes. R actualmente nos permite hacer distintas tareas comó análisis estadísticos, generación de gráficos, escritura de documentos, desarrollo de aplicaciones webs, etc. RStudio. RStudio es un entorno de desarrollo integrado para el lenguaje de programación R, dedicado la computación estadística y gráficos. Git. Git es un software de control de versiones. Esta pensando en la eficiencia y la confiabilidad del mantenimiento de versiones de aplicaciones. Git nos permitirá usar bash en windows través del terminal en RStudio.","code":""},{"path":[]},{"path":[]},{"path":"https://inkaverse.com/articles/rticles.html","id":"herramientas-adicionales","dir":"Articles","previous_headings":"Herramientas para documentos reproducibles","what":"Herramientas adicionales","title":"Rticles","text":"Existen alguna herramientas básicas que deben faltar en tú computador: Chrome (buscador web) Foxit Reader (lector de PDFs) WinRAR (compression/descompresor de archivos) Google Backup Sync (servicio de sincronización de datos) ShareX (herramienta para captura de pantalla) Los usuarios de Windows, pueden instalar estas aplicaciones entre otras desde ninite.","code":""},{"path":"https://inkaverse.com/articles/rticles.html","id":"chocolatey-opcional","dir":"Articles","previous_headings":"Herramientas para documentos reproducibles","what":"Chocolatey (opcional)","title":"Rticles","text":"Si eres usuario de windows, puedes instalar todas las herramientas mencionadas desde el administrador de paquetes chocolatey través de PowerShell.","code":"open https://chocolatey.org/packages Start-Process powershell -Verb runAs Set-ExecutionPolicy Bypass -Scope Process -Force; [System.Net.ServicePointManager]::SecurityProtocol = [System.Net.ServicePointManager]::SecurityProtocol -bor 3072; iex ((New-Object System.Net.WebClient).DownloadString('https://chocolatey.org/install.ps1')) choco install avastfreeantivirus choco install googlechrome choco install winrar choco install zotero choco install r choco install rtools choco install r.studio choco install git choco install google-backup-and-sync choco install foxitreader choco install sharex choco install k-litecodecpackfull choco install gom-player choco install aimp choco install teamviewer"},{"path":"https://inkaverse.com/articles/tarpuy.html","id":"módulos","dir":"Articles","previous_headings":"","what":"Módulos","title":"Tarpuy","text":"Módulos de la aplicación Tarpuy","code":""},{"path":"https://inkaverse.com/articles/yupana.html","id":"base-de-datos","dir":"Articles","previous_headings":"","what":"Base de datos","title":"Yupana","text":"Los datos deben estar organizado en formato tidy-data. Tener en cuenta algunas consideraciones: usar caracteres extraños en la cabeceras, e..: %, #, &, $, °, !, ^, etc Los datos deben iniciar en la primera fila y columna, e.. A1 Evitar usar espacio entre los nombres de las variables, en reemplazo pueden usar “_” o “.” Las columnas que esten entre corchetes “[]” serán excluidas del análisis","code":""},{"path":"https://inkaverse.com/articles/yupana.html","id":"módulos","dir":"Articles","previous_headings":"","what":"Módulos","title":"Yupana","text":"Table 1: Módulos de la aplicación Yupana","code":""},{"path":"https://inkaverse.com/articles/yupana.html","id":"graphics","dir":"Articles","previous_headings":"","what":"Graphics","title":"Yupana","text":"Los parámetros de los gráficos generados en la app pueden ser guardadas en hojas de cálculo de google y luego pueden ser cargadas (Table 2).","code":""},{"path":"https://inkaverse.com/articles/yupana.html","id":"opciones-de-gráfico","dir":"Articles","previous_headings":"Graphics","what":"Opciones de gráfico","title":"Yupana","text":"Table 2: Lista de argumentos, descripción y opciones para la generación de gráficos en la aplicación Yupana Nota: Opciones basadas en la función: plot_smr()","code":""},{"path":"https://inkaverse.com/articles/yupana.html","id":"argumentos-y-valores","dir":"Articles","previous_headings":"Graphics > Opciones de gráfico","what":"Argumentos y valores","title":"Yupana","text":"Figure 1: Parámetros en {arguments} y {values} para la generación de gráficos en la aplicación Yupana. Figure 2: Figura basada en los {arguments} y {values} de la tabla anterior. La apliación por defecto genera un gama de colores {colors} en una escala de grises. Los colores pueden ser modificados de forma manual por sus nombres en ingles o usando los valores HEX. En este caso se cambió la escala de grises por los colores verde (green) y rojo (red) (Figure 1, 2).","code":""},{"path":"https://inkaverse.com/articles/yupana.html","id":"incluir-nuevas-capas-opt","dir":"Articles","previous_headings":"Graphics","what":"Incluir nuevas capas opt","title":"Yupana","text":"Yupana partir de la versión 0.2.0 permite la inclusión de capas adicionales los gráficos. Puedes incluir dicha información en opt de los {arguments} (Figure 3, 4). Puedes incluir diversas capas descritas para el paquete ggplot2. Figure 3: Gráfico con la inclusión de la capa facet_grid() Figure 4: Inclusión de facet_grid(tratamiento ~ .) en opt de los {arguments} en Yupana.","code":""},{"path":"https://inkaverse.com/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Flavio Lozano-Isla. Author, maintainer. . Contributor. . Copyright holder.","code":""},{"path":"https://inkaverse.com/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Lozano-Isla F (2022). inti: Tools Statistical Procedures Plant Science. R package version 0.5.6, https://CRAN.R-project.org/package=inti.","code":"@Manual{, title = {{inti}: Tools and Statistical Procedures in Plant Science}, author = {Flavio Lozano-Isla}, year = {2022}, note = {R package version 0.5.6}, url = {https://CRAN.R-project.org/package=inti}, }"},{"path":"https://inkaverse.com/index.html","id":"inti-","dir":"","previous_headings":"","what":"Inkaverse","title":"Inkaverse","text":"‘inti’ package part ‘inkaverse’ project developing different procedures tools used plant science experimental designs. mean aim package support researchers planning experiments data collection ‘tarpuy()’, data analysis graphics ‘yupana()’, technical writing. Learn ‘inkaverse’ project https://inkaverse.com/.","code":""},{"path":"https://inkaverse.com/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Inkaverse","text":"install stable version CRAN: install latest development version directly GitHub: need install specific version:","code":"install.packages(\"inti\") if (!require(\"remotes\")) install.packages(\"remotes\") remotes::install_github(\"flavjack/inti\") if (!require(\"remotes\")) install.packages(\"remotes\") remotes::install_version(\"inti\", version = \"0.4.4\")"},{"path":"https://inkaverse.com/index.html","id":"shiny-apps","dir":"","previous_headings":"","what":"Shiny apps","title":"Inkaverse","text":"first time running apps consider install app dependencies: install package app dependencies also can access apps Addins list Rstudio running following code:","code":"inti::yupana(dependencies = TRUE)"},{"path":"https://inkaverse.com/index.html","id":"yupana","dir":"","previous_headings":"Shiny apps","what":"Yupana","title":"Inkaverse","text":"","code":"inti::yupana()"},{"path":"https://inkaverse.com/index.html","id":"tarpuy","dir":"","previous_headings":"Shiny apps","what":"Tarpuy","title":"Inkaverse","text":"","code":"inti::tarpuy()"},{"path":"https://inkaverse.com/reference/colortext.html","id":null,"dir":"Reference","previous_headings":"","what":"Colourise text for display in the terminal — colortext","title":"Colourise text for display in the terminal — colortext","text":"R currently running system supports terminal colours text returned unchanged.","code":""},{"path":"https://inkaverse.com/reference/colortext.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Colourise text for display in the terminal — colortext","text":"","code":"colortext(text, fg = \"red\", bg = NULL)"},{"path":"https://inkaverse.com/reference/colortext.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Colourise text for display in the terminal — colortext","text":"text character vector fg foreground colour, defaults white bg background colour, defaults transparent","code":""},{"path":"https://inkaverse.com/reference/colortext.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Colourise text for display in the terminal — colortext","text":"Allowed colours : black, blue, brown, cyan, dark gray, green, light blue, light cyan, light gray, light green, light purple, light red, purple, red, white, yellow","code":""},{"path":"https://inkaverse.com/reference/colortext.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Colourise text for display in the terminal — colortext","text":"testthat package","code":""},{"path":"https://inkaverse.com/reference/colortext.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Colourise text for display in the terminal — colortext","text":"","code":"print(colortext(\"Red\", \"red\")) #> [1] \"\\033[0;31mRed\\033[0m\" cat(colortext(\"Red\", \"red\"), \"\\n\") #> Red cat(colortext(\"White on red\", \"white\", \"red\"), \"\\n\") #> White on red"},{"path":"https://inkaverse.com/reference/figure2qmd.html","id":null,"dir":"Reference","previous_headings":"","what":"Figure to Rmarkdown — figure2qmd","title":"Figure to Rmarkdown — figure2qmd","text":"Use Articul8 Add-ons Google docs build Rticles","code":""},{"path":"https://inkaverse.com/reference/figure2qmd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Figure to Rmarkdown — figure2qmd","text":"","code":"figure2qmd(text, path = \".\", opts = NA)"},{"path":"https://inkaverse.com/reference/figure2qmd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Figure to Rmarkdown — figure2qmd","text":"text Markdown text figure information [string] path Image path figures [path: \".\" (base directory)] opts chunk options brackets [string: NA]","code":""},{"path":"https://inkaverse.com/reference/figure2qmd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Figure to Rmarkdown — figure2qmd","text":"string mutated","code":""},{"path":"https://inkaverse.com/reference/figure2qmd.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Figure to Rmarkdown — figure2qmd","text":"Quarto option can included title using \"\" separated commas","code":""},{"path":"https://inkaverse.com/reference/figure2rmd.html","id":null,"dir":"Reference","previous_headings":"","what":"Figure to Rmarkdown — figure2rmd","title":"Figure to Rmarkdown — figure2rmd","text":"Use Articul8 Add-ons Google docs build Rticles","code":""},{"path":"https://inkaverse.com/reference/figure2rmd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Figure to Rmarkdown — figure2rmd","text":"","code":"figure2rmd(text, path = \".\", opts = NA, prefix = \"Figure\")"},{"path":"https://inkaverse.com/reference/figure2rmd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Figure to Rmarkdown — figure2rmd","text":"text String table information path Path image fot figure opts chunk options brackets. prefix Prefix name figure","code":""},{"path":"https://inkaverse.com/reference/figure2rmd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Figure to Rmarkdown — figure2rmd","text":"Mutated string","code":""},{"path":"https://inkaverse.com/reference/footnotes.html","id":null,"dir":"Reference","previous_headings":"","what":"Footnotes in tables — footnotes","title":"Footnotes in tables — footnotes","text":"Include tables footnotes symbols kables pandoc format","code":""},{"path":"https://inkaverse.com/reference/footnotes.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Footnotes in tables — footnotes","text":"","code":"footnotes(table, notes = NULL, label = \"Note:\", notation = \"alphabet\")"},{"path":"https://inkaverse.com/reference/footnotes.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Footnotes in tables — footnotes","text":"table Kable output pandoc format. notes Footnotes table. label Label start footnote. notation Notation footnotes (default = \"alphabet\"). See details.","code":""},{"path":"https://inkaverse.com/reference/footnotes.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Footnotes in tables — footnotes","text":"Table footnotes word html documents","code":""},{"path":"https://inkaverse.com/reference/footnotes.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Footnotes in tables — footnotes","text":"use pandoc format kable(format = \"pipe\"). can add footnote symbol using {hypen} table. notation use: \"alphabet\", \"number\", \"symbol\", \"none\".","code":""},{"path":"https://inkaverse.com/reference/gdoc2qmd.html","id":null,"dir":"Reference","previous_headings":"","what":"Google docs to Rmarkdown — gdoc2qmd","title":"Google docs to Rmarkdown — gdoc2qmd","text":"Use Articul8 Add-ons Google docs build Rticles","code":""},{"path":"https://inkaverse.com/reference/gdoc2qmd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Google docs to Rmarkdown — gdoc2qmd","text":"","code":"gdoc2qmd(file, export = NA)"},{"path":"https://inkaverse.com/reference/gdoc2qmd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Google docs to Rmarkdown — gdoc2qmd","text":"file Zip file path Articul8 exported md format [path] export Path export files [path: NA (file directory)]","code":""},{"path":"https://inkaverse.com/reference/gdoc2qmd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Google docs to Rmarkdown — gdoc2qmd","text":"path","code":""},{"path":"https://inkaverse.com/reference/gdoc2qmd.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Google docs to Rmarkdown — gdoc2qmd","text":"add \"> END\" replace \"knitr::knit_exit()\"","code":""},{"path":"https://inkaverse.com/reference/gdoc2rmd.html","id":null,"dir":"Reference","previous_headings":"","what":"Google docs to Rmarkdown — gdoc2rmd","title":"Google docs to Rmarkdown — gdoc2rmd","text":"Use Articul8 Add-ons Google docs build Rticles","code":""},{"path":"https://inkaverse.com/reference/gdoc2rmd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Google docs to Rmarkdown — gdoc2rmd","text":"","code":"gdoc2rmd(file, export = \"files\", prefix_fig = \"Figure\", prefix_tab = \"Table\")"},{"path":"https://inkaverse.com/reference/gdoc2rmd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Google docs to Rmarkdown — gdoc2rmd","text":"file Zip file path Articul8 exported md format export path export files. Default file directory prefix_fig Prefix name figure prefix_tab Prefix name table","code":""},{"path":"https://inkaverse.com/reference/gdoc2rmd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Google docs to Rmarkdown — gdoc2rmd","text":"folder","code":""},{"path":"https://inkaverse.com/reference/H2cal.html","id":null,"dir":"Reference","previous_headings":"","what":"Broad-sense heritability in plant breeding — H2cal","title":"Broad-sense heritability in plant breeding — H2cal","text":"Heritability plant breeding genotype difference basis","code":""},{"path":"https://inkaverse.com/reference/H2cal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Broad-sense heritability in plant breeding — H2cal","text":"","code":"H2cal( data, trait, gen.name, rep.n, env.n = 1, year.n = 1, env.name = NULL, year.name = NULL, fixed.model, random.model, summary = FALSE, emmeans = FALSE, weights = NULL, plot_diag = FALSE, outliers.rm = FALSE, trial = NULL )"},{"path":"https://inkaverse.com/reference/H2cal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Broad-sense heritability in plant breeding — H2cal","text":"data Experimental design data frame factors traits. trait Name trait. gen.name Name genotypes. rep.n Number replications experiment. env.n Number environments (default = 1). See details. year.n Number years (default = 1). See details. env.name Name environments (default = NULL). See details. year.name Name years (default = NULL). See details. fixed.model fixed effects model (BLUEs). See examples. random.model random effects model (BLUPs). See examples. summary Print summary random model (default = FALSE). emmeans Use emmeans calculate BLUEs (default = FALSE). weights optional vector ‘prior weights’ used fitting process (default = NULL). plot_diag Show diagnostic plots fixed random effects (default = FALSE). Options: \"base\", \"ggplot\". . outliers.rm Remove outliers (default = FALSE). See references. trial Column name trial results (default = NULL).","code":""},{"path":"https://inkaverse.com/reference/H2cal.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Broad-sense heritability in plant breeding — H2cal","text":"list","code":""},{"path":"https://inkaverse.com/reference/H2cal.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Broad-sense heritability in plant breeding — H2cal","text":"function allows made calculation individual multi-environmental trials (MET) using fixed random model. 1. variance components based random model population summary information based fixed model (BLUEs). 2. Heritability three approaches: Standard (ANOVA), Cullis (BLUPs) Piepho (BLUEs). 3. Best Linear Unbiased Estimators (BLUEs), fixed effect. 4. Best Linear Unbiased Predictors (BLUPs), random effect. 5. Table outliers removed model. individual experiments necessary provide trait, gen.name, rep.n. MET experiments env.n env.name /year.n year.name according experiment. BLUEs calculation based pairwise comparison time consuming increase number genotypes. can specify emmeans = FALSE calculate BLUEs faster. emmeans = FALSE change 1 0 fixed model exclude intersect analysis get genotypes BLUEs. information review references.","code":""},{"path":"https://inkaverse.com/reference/H2cal.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Broad-sense heritability in plant breeding — H2cal","text":"Bernal Vasquez, Angela Maria, et al. “Outlier Detection Methods Generalized Lattices: Case Study Transition ANOVA REML.” Theoretical Applied Genetics, vol. 129, . 4, Apr. 2016. Buntaran, H., Piepho, H., Schmidt, P., Ryden, J., Halling, M., Forkman, J. (2020). Cross validation stagewise mixed model analysis Swedish variety trials winter wheat spring barley. Crop Science, 60(5). Schmidt, P., J. Hartung, J. Bennewitz, H.P. Piepho. 2019. Heritability Plant Breeding Genotype Difference Basis. Genetics 212(4). Schmidt, P., J. Hartung, J. Rath, H.P. Piepho. 2019. Estimating Broad Sense Heritability Unbalanced Data Agricultural Cultivar Trials. Crop Science 59(2). Tanaka, E., Hui, F. K. C. (2019). Symbolic Formulae Linear Mixed Models. H. Nguyen (Ed.), Statistics Data Science. Springer. Zystro, J., Colley, M., Dawson, J. (2018). Alternative Experimental Designs Plant Breeding. Plant Breeding Reviews. John Wiley Sons, Ltd.","code":""},{"path":"https://inkaverse.com/reference/H2cal.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Broad-sense heritability in plant breeding — H2cal","text":"Maria Belen Kistner Flavio Lozano Isla","code":""},{"path":"https://inkaverse.com/reference/H2cal.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Broad-sense heritability in plant breeding — H2cal","text":"","code":"library(inti) dt <- potato hr <- H2cal(data = dt , trait = \"stemdw\" , gen.name = \"geno\" , rep.n = 5 , fixed.model = \"0 + (1|bloque) + geno\" , random.model = \"1 + (1|bloque) + (1|geno)\" , emmeans = TRUE , plot_diag = FALSE , outliers.rm = TRUE ) hr$tabsmr #> trait rep geno env year mean std min max V.g V.e #> 1 stemdw 5 15 1 1 12.59867 4.749994 2.818 22.302 19.96002 9.410932 #> V.p repeatability H2.s H2.p H2.c #> 1 21.84221 0.913828 0.913828 0.9502395 0.9533473 hr$blues #> # A tibble: 15 × 6 #> geno stemdw SE df lower.CL upper.CL #> #> 1 G01 15.7 1.03 120. 13.7 17.8 #> 2 G02 10.1 1.03 120. 8.08 12.2 #> 3 G03 9.70 1.03 120. 7.65 11.7 #> 4 G04 15.2 1.03 120. 13.1 17.2 #> 5 G05 12.9 1.09 123. 10.7 15.0 #> 6 G06 22.3 1.03 120. 20.3 24.3 #> 7 G07 2.82 1.03 120. 0.778 4.86 #> 8 G08 10.4 1.03 120. 8.38 12.5 #> 9 G09 15.7 1.03 120. 13.6 17.7 #> 10 G10 9.24 1.03 120. 7.20 11.3 #> 11 G11 6.42 1.03 120. 4.38 8.47 #> 12 G12 16.1 1.03 120. 14.1 18.2 #> 13 G13 14.6 1.03 120. 12.6 16.7 #> 14 G14 16.3 1.03 120. 14.3 18.3 #> 15 G15 11.5 1.03 120. 9.43 13.5 hr$blups #> # A tibble: 15 × 2 #> geno stemdw #> #> 1 G01 15.6 #> 2 G02 10.2 #> 3 G03 9.82 #> 4 G04 15.1 #> 5 G05 12.8 #> 6 G06 20.6 #> 7 G07 3.25 #> 8 G08 10.5 #> 9 G09 15.5 #> 10 G10 9.39 #> 11 G11 6.70 #> 12 G12 15.9 #> 13 G13 14.5 #> 14 G14 16.1 #> 15 G15 11.5 hr$outliers #> $fixed #> bloque geno stemdw resi res_MAD rawp.BHStud index adjp bholm out_flag #> 68 IV G05 80.65 60.36709 18.84505 0 68 0 0 OUTLIER #> #> $random #> bloque geno stemdw resi res_MAD rawp.BHStud index adjp #> 68 IV G05 80.65 61.39925 18.886676 0.0000000000 68 0.0000000000 #> 100 IV G06 33.52 12.02340 3.698449 0.0002169207 100 0.0002169207 #> bholm out_flag #> 68 0.00000000 OUTLIER #> 100 0.03232119 OUTLIER #>"},{"path":"https://inkaverse.com/reference/include_figure.html","id":null,"dir":"Reference","previous_headings":"","what":"Figure with caption and notes — include_figure","title":"Figure with caption and notes — include_figure","text":"Include figures title notes using data base","code":""},{"path":"https://inkaverse.com/reference/include_figure.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Figure with caption and notes — include_figure","text":"","code":"include_figure(figure, caption = NA, notes = NA, label = NA)"},{"path":"https://inkaverse.com/reference/include_figure.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Figure with caption and notes — include_figure","text":"figure Path URL figure. caption Figure caption (default = NA). notes Figure notes (default = NA). label Label notes (default = NA).","code":""},{"path":"https://inkaverse.com/reference/include_figure.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Figure with caption and notes — include_figure","text":"Figure caption notes","code":""},{"path":"https://inkaverse.com/reference/include_figure.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Figure with caption and notes — include_figure","text":"","code":"library(inti) figure <- \"https://inkaverse.com/reference/figures/logo.png\" figure %>% include_figure(caption = \"Title test.\" , notes = \"Note test.\") #> $caption #> [1] \"Title test. Note test.\" #> #> $path #> [1] \"https://inkaverse.com/reference/figures/logo.png\" #> #> $figure #> [1] \"https://inkaverse.com/reference/figures/logo.png\" #> attr(,\"class\") #> [1] \"knit_image_paths\" \"knit_asis\" #>"},{"path":"https://inkaverse.com/reference/include_table.html","id":null,"dir":"Reference","previous_headings":"","what":"Table with footnotes — include_table","title":"Table with footnotes — include_table","text":"Include tables title footnotes word html documents","code":""},{"path":"https://inkaverse.com/reference/include_table.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Table with footnotes — include_table","text":"","code":"include_table(table, caption = NA, notes = NA, label = NA, notation = \"none\")"},{"path":"https://inkaverse.com/reference/include_table.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Table with footnotes — include_table","text":"table Data frame. caption Table caption (default = NULL). See details. notes Footnotes table (default = NA). See details. label Label start footnote (default = NA). notation Notation symbols footnotes (default = \"none\") Others: \"alphabet\", \"number\", \"symbol\".","code":""},{"path":"https://inkaverse.com/reference/include_table.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Table with footnotes — include_table","text":"Table caption footnotes","code":""},{"path":"https://inkaverse.com/reference/include_table.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Table with footnotes — include_table","text":"","code":"library(inti) table <- data.frame( x = rep_len(1, 5) , y = rep_len(3, 5) , z = rep_len(\"c\", 5) ) table %>% inti::include_table( caption = \"Title caption b) line 0 a) line 1 b) line 2\" , notes = \"Footnote\" , label = \"Where:\" ) #> #> #> Table: Title caption b) line 0 a) line 1 b) line 2 #> #> | x| y|z | #> |--:|--:|:--| #> | 1| 3|c | #> | 1| 3|c | #> | 1| 3|c | #> | 1| 3|c | #> | 1| 3|c | #> #> Where:<\/small> #> Footnote<\/small>"},{"path":"https://inkaverse.com/reference/jc_tombola.html","id":null,"dir":"Reference","previous_headings":"","what":"Journal Club Tombola — jc_tombola","title":"Journal Club Tombola — jc_tombola","text":"Function arrange journal club schedule","code":""},{"path":"https://inkaverse.com/reference/jc_tombola.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Journal Club Tombola — jc_tombola","text":"","code":"jc_tombola( data, members, papers = 1, group = NA, gr_lvl = NA, status = NA, st_lvl = \"active\", frq = 7, date = NA, seed = NA )"},{"path":"https://inkaverse.com/reference/jc_tombola.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Journal Club Tombola — jc_tombola","text":"data Data frame withe members information. members Columns members names. papers Number paper meeting group Column arrange group. gr_lvl Levels groups arrange. See details. status Column status members. st_lvl Level confirm assistance JC. See details. frq Number day session. date Date start first session JC. seed Number replicate results (default = date).","code":""},{"path":"https://inkaverse.com/reference/jc_tombola.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Journal Club Tombola — jc_tombola","text":"data frame schedule JC","code":""},{"path":"https://inkaverse.com/reference/jc_tombola.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Journal Club Tombola — jc_tombola","text":"function consider n levels gr_lvl. case levels using \"\" \"\" combination. suggested levels st_lvl : active spectator. \"active\" members enter schedule.","code":""},{"path":"https://inkaverse.com/reference/mean_comparison.html","id":null,"dir":"Reference","previous_headings":"","what":"Mean comparison test — mean_comparison","title":"Mean comparison test — mean_comparison","text":"Function compare treatment lm aov using data frames","code":""},{"path":"https://inkaverse.com/reference/mean_comparison.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Mean comparison test — mean_comparison","text":"","code":"mean_comparison( data, response, model_factors, comparison, test_comp = \"SNK\", sig_level = 0.05 )"},{"path":"https://inkaverse.com/reference/mean_comparison.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Mean comparison test — mean_comparison","text":"data Fieldbook data. response Model used experimental design. model_factors Factor model. comparison Significance level analysis (default = 0.05). test_comp Comparison test (default = \"SNK\"). Others: \"TUKEY\", \"DUNCAN\". sig_level Significance level analysis (default = 0.05).","code":""},{"path":"https://inkaverse.com/reference/mean_comparison.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Mean comparison test — mean_comparison","text":"list","code":""},{"path":"https://inkaverse.com/reference/mean_comparison.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Mean comparison test — mean_comparison","text":"","code":"if (FALSE) { library(inti) library(gsheet) url <- paste0(\"https://docs.google.com/spreadsheets/d/\" , \"15r7ZwcZZHbEgltlF6gSFvCTFA-CFzVBWwg3mFlRyKPs/\" , \"edit#gid=172957346\") # browseURL(url) fb <- gsheet2tbl(url) mc <- mean_comparison(data = fb , response = \"spad_29\" , model_factors = \"bloque* geno*treat\" , comparison = c(\"geno\", \"treat\") , test_comp = \"SNK\" ) mc$comparison mc$stat }"},{"path":"https://inkaverse.com/reference/met.html","id":null,"dir":"Reference","previous_headings":"","what":"Swedish cultivar trial data — met","title":"Swedish cultivar trial data — met","text":"datasets obtained official Swedish cultivar tests. Dry matter yield analyzed. trials laid alpha-designs two replicates. Within replicate, five seven incomplete blocks.","code":""},{"path":"https://inkaverse.com/reference/met.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Swedish cultivar trial data — met","text":"","code":"met"},{"path":"https://inkaverse.com/reference/met.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Swedish cultivar trial data — met","text":"data frame 1069 rows 8 variables: zone Sweden divided three different agricultural zones: South, Middle, North location Locations: 18 location Zones rep Replications (4): number replication experiment alpha Incomplete blocks (8) alpha-designs cultivar Cultivars (30): genotypes evaluated yield Yield kg/ha year Year (1): 2016 env enviroment (18): combination zone + location + year","code":""},{"path":"https://inkaverse.com/reference/met.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Swedish cultivar trial data — met","text":"doi:10.1002/csc2.20177","code":""},{"path":"https://inkaverse.com/reference/metamorphosis.html","id":null,"dir":"Reference","previous_headings":"","what":"Transform fieldbooks based in a dictionary — metamorphosis","title":"Transform fieldbooks based in a dictionary — metamorphosis","text":"Transform entire fieldbook according data dictionary","code":""},{"path":"https://inkaverse.com/reference/metamorphosis.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Transform fieldbooks based in a dictionary — metamorphosis","text":"","code":"metamorphosis(fieldbook, dictionary, from, to, index, colnames)"},{"path":"https://inkaverse.com/reference/metamorphosis.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Transform fieldbooks based in a dictionary — metamorphosis","text":"fieldbook Data frame original information. dictionary Data frame new names categories. See details. Column dictionary original names. Column dictionary new names. index Column dictionary type level variables. colnames Character vector name columns.","code":""},{"path":"https://inkaverse.com/reference/metamorphosis.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Transform fieldbooks based in a dictionary — metamorphosis","text":"List two objects. 1. New data frame. 2. Dictionary.","code":""},{"path":"https://inkaverse.com/reference/metamorphosis.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Transform fieldbooks based in a dictionary — metamorphosis","text":"function require least three columns. 1. Original names (). 2. New names (). 3. Variable type (index).","code":""},{"path":"https://inkaverse.com/reference/outliers_remove.html","id":null,"dir":"Reference","previous_headings":"","what":"Remove outliers — outliers_remove","title":"Remove outliers — outliers_remove","text":"Use method M4 Bernal Vasquez (2016). Bonferroni Holm test judge residuals standardized re scaled MAD (BH MADR).","code":""},{"path":"https://inkaverse.com/reference/outliers_remove.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Remove outliers — outliers_remove","text":"","code":"outliers_remove(data, trait, model, drop_na = TRUE)"},{"path":"https://inkaverse.com/reference/outliers_remove.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Remove outliers — outliers_remove","text":"data Experimental design data frame factors traits. trait Name trait. model fixed random effects model. drop_na drop NA values data.frame","code":""},{"path":"https://inkaverse.com/reference/outliers_remove.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Remove outliers — outliers_remove","text":"list. 1. Table date without outliers. 2. outliers dataset.","code":""},{"path":"https://inkaverse.com/reference/outliers_remove.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Remove outliers — outliers_remove","text":"Function remove outliers MET experiments","code":""},{"path":"https://inkaverse.com/reference/outliers_remove.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Remove outliers — outliers_remove","text":"Bernal Vasquez, Angela Maria, et al. “Outlier Detection Methods Generalized Lattices: Case Study Transition ANOVA REML.” Theoretical Applied Genetics, vol. 129, . 4, Apr. 2016.","code":""},{"path":"https://inkaverse.com/reference/outliers_remove.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Remove outliers — outliers_remove","text":"","code":"library(inti) rmout <- outliers_remove( data = potato , trait =\"stemdw\" , model = \"0 + treat*geno + (1|bloque) + geno\" , drop_na = FALSE ) rmout #> $data #> treat geno bloque stemdw #> 1 sequia G01 II 14.87 #> 2 sequia G02 IV 8.63 #> 3 irrigado G01 III NA #> 4 sequia G02 I 6.58 #> 5 irrigado G03 II 12.63 #> 6 irrigado G04 V 17.46 #> 7 irrigado G01 I 15.32 #> 8 irrigado G05 IV 14.55 #> 9 sequia G06 II 21.19 #> 10 sequia G05 I NA #> 11 irrigado G01 II 18.13 #> 12 sequia G07 II 3.70 #> 13 irrigado G08 II 12.48 #> 14 irrigado G06 III 29.49 #> 15 irrigado G09 III 16.96 #> 16 irrigado G10 II 8.20 #> 17 sequia G11 I 7.90 #> 18 sequia G12 III 9.19 #> 19 irrigado G07 I 2.48 #> 20 irrigado G04 II 20.75 #> 21 irrigado G13 II 18.97 #> 22 irrigado G14 III 14.57 #> 23 irrigado G04 IV 18.84 #> 24 sequia G04 V 8.79 #> 25 sequia G08 V 8.17 #> 26 sequia G04 III 12.53 #> 27 sequia G01 IV 16.26 #> 28 irrigado G10 I 11.19 #> 29 irrigado G08 V 11.18 #> 30 irrigado G02 V 12.14 #> 31 irrigado G07 III 4.78 #> 32 irrigado G08 I 12.52 #> 33 irrigado G14 V 23.96 #> 34 irrigado G03 I 11.18 #> 35 sequia G13 III 7.79 #> 36 sequia G01 V 11.97 #> 37 sequia G03 I 9.03 #> 38 irrigado G15 III 11.17 #> 39 irrigado G03 IV 12.20 #> 40 irrigado G09 IV 18.17 #> 41 irrigado G11 II 4.90 #> 42 sequia G03 V 8.73 #> 43 sequia G11 III 5.56 #> 44 irrigado G06 V 23.77 #> 45 sequia G05 V NA #> 46 sequia G08 IV 8.44 #> 47 irrigado G11 IV 7.53 #> 48 sequia G11 II 3.11 #> 49 irrigado G10 III 14.77 #> 50 sequia G06 IV 17.45 #> 51 sequia G09 I 13.36 #> 52 irrigado G11 I 7.27 #> 53 sequia G11 IV 5.72 #> 54 irrigado G15 IV 11.76 #> 55 irrigado G13 IV 19.83 #> 56 sequia G14 V 12.94 #> 57 irrigado G02 IV 14.01 #> 58 irrigado G09 II 19.20 #> 59 irrigado G02 III 12.12 #> 60 sequia G08 III 10.10 #> 61 irrigado G06 II 24.35 #> 62 sequia G13 IV 11.52 #> 63 sequia G14 III 13.37 #> 64 sequia G04 II 15.02 #> 65 irrigado G11 III 10.32 #> 66 irrigado G07 II 1.71 #> 67 irrigado G08 IV 14.28 #> 68 sequia G05 IV NA #> 69 irrigado G04 I 12.80 #> 70 irrigado G11 V 7.99 #> 71 irrigado G12 I 19.60 #> 72 sequia G14 IV 13.97 #> 73 sequia G07 III 3.09 #> 74 irrigado G03 III 8.56 #> 75 sequia G01 I 10.44 #> 76 sequia G04 I 13.73 #> 77 sequia G03 II 8.33 #> 78 irrigado G15 II 11.78 #> 79 sequia G12 IV 12.30 #> 80 sequia G12 I 13.91 #> 81 sequia G08 I 5.14 #> 82 sequia G05 II NA #> 83 sequia G02 II 8.46 #> 84 sequia G10 I 9.84 #> 85 sequia G15 I 11.43 #> 86 irrigado G07 V 1.71 #> 87 sequia G10 V 6.36 #> 88 sequia G13 II 12.34 #> 89 sequia G07 V 2.71 #> 90 sequia G03 III 7.16 #> 91 sequia G15 IV 11.19 #> 92 sequia G13 I 12.23 #> 93 sequia G03 IV 8.37 #> 94 irrigado G10 V 11.74 #> 95 sequia G13 V 11.82 #> 96 sequia G09 II 17.02 #> 97 irrigado G14 IV 17.89 #> 98 irrigado G01 V 13.80 #> 99 sequia G01 III 15.37 #> 100 irrigado G06 IV 33.52 #> 101 sequia G04 IV 12.56 #> 102 irrigado G15 V 12.13 #> 103 irrigado G13 III 17.36 #> 104 irrigado G02 II 12.58 #> 105 sequia G08 II 10.31 #> 106 irrigado G04 III 19.29 #> 107 sequia G02 V 8.39 #> 108 sequia G06 V 13.12 #> 109 irrigado G15 I 12.14 #> 110 irrigado G13 V 18.16 #> 111 irrigado G05 V 12.03 #> 112 sequia G09 III 16.71 #> 113 sequia G09 V 10.97 #> 114 sequia G10 II 7.44 #> 115 irrigado G07 IV 4.06 #> 116 irrigado G05 I 13.07 #> 117 irrigado G02 I 8.54 #> 118 sequia G05 III NA #> 119 irrigado G12 II 17.81 #> 120 sequia G15 III 10.95 #> 121 irrigado G13 I 16.27 #> 122 sequia G14 II 17.86 #> 123 sequia G12 II 16.82 #> 124 sequia G15 II 11.82 #> 125 irrigado G09 V 14.22 #> 126 sequia G06 I 16.22 #> 127 sequia G09 IV 14.02 #> 128 sequia G15 V 10.32 #> 129 irrigado G14 I 19.93 #> 130 sequia G06 III 17.45 #> 131 irrigado G01 IV 16.97 #> 132 irrigado G12 III 19.78 #> 133 sequia G12 V 14.22 #> 134 irrigado G12 V 17.61 #> 135 sequia G11 V 3.95 #> 136 irrigado G12 IV 19.87 #> 137 irrigado G09 I 16.05 #> 138 sequia G02 III 9.76 #> 139 sequia G07 I 2.97 #> 140 irrigado G08 III 11.61 #> 141 irrigado G06 I 26.46 #> 142 irrigado G10 IV NA #> 143 irrigado G03 V 10.76 #> 144 sequia G07 IV 0.97 #> 145 irrigado G05 III 15.19 #> 146 sequia G14 I 10.62 #> 147 sequia G10 III 11.27 #> 148 irrigado G14 II 17.86 #> 149 irrigado G05 II 16.57 #> 150 sequia G10 IV 6.58 #> #> $outliers #> treat geno bloque stemdw resi res_MAD rawp.BHStud index #> 3 irrigado G01 III 24.19 6.520276 4.031041 5.553035e-05 3 #> 10 sequia G05 I 11.14 -13.467719 -8.326170 0.000000e+00 10 #> 45 sequia G05 V 11.52 -13.006525 -8.041046 8.881784e-16 45 #> 68 sequia G05 IV 80.65 54.860861 33.916722 0.000000e+00 68 #> 82 sequia G05 II 11.65 -13.422893 -8.298457 0.000000e+00 82 #> 118 sequia G05 III 10.02 -14.963724 -9.251048 0.000000e+00 118 #> 142 irrigado G10 IV 5.03 -5.949139 -3.677946 2.351195e-04 142 #> adjp bholm out_flag #> 3 5.553035e-05 8.051901e-03 OUTLIER #> 10 0.000000e+00 0.000000e+00 OUTLIER #> 45 8.881784e-16 1.296740e-13 OUTLIER #> 68 0.000000e+00 0.000000e+00 OUTLIER #> 82 0.000000e+00 0.000000e+00 OUTLIER #> 118 0.000000e+00 0.000000e+00 OUTLIER #> 142 2.351195e-04 3.385720e-02 OUTLIER #>"},{"path":"https://inkaverse.com/reference/plot_diag.html","id":null,"dir":"Reference","previous_headings":"","what":"Diagnostic plots — plot_diag","title":"Diagnostic plots — plot_diag","text":"Function plot diagnostic models","code":""},{"path":"https://inkaverse.com/reference/plot_diag.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Diagnostic plots — plot_diag","text":"","code":"plot_diag(model, title = NA)"},{"path":"https://inkaverse.com/reference/plot_diag.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Diagnostic plots — plot_diag","text":"model Statistical model title Plot title","code":""},{"path":"https://inkaverse.com/reference/plot_diag.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Diagnostic plots — plot_diag","text":"plots","code":""},{"path":"https://inkaverse.com/reference/plot_diag.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Diagnostic plots — plot_diag","text":"","code":"if (FALSE) { dt <- potato lm <- aov(stemdw ~ bloque + geno*treat, dt) #lm <- lme4::lmer(stemdw ~ bloque + (1|geno:treat), dt) plot(lm, which = 1) plot_diag(lm)[3] plot(lm, which = 2) plot_diag(lm)[2] plot(lm, which = 3) plot_diag(lm)[4] plot(lm, which = 4) plot_diag(lm)[1] }"},{"path":"https://inkaverse.com/reference/plot_raw.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot raw data — plot_raw","title":"Plot raw data — plot_raw","text":"Function use raw data made boxplot graphic","code":""},{"path":"https://inkaverse.com/reference/plot_raw.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot raw data — plot_raw","text":"","code":"plot_raw( data, type = \"boxplot\", x, y, group = NULL, xlab = NULL, ylab = NULL, glab = NULL, ylimits = NULL, xlimits = NULL, xrotation = NULL, legend = \"top\", xtext = NULL, gtext = NULL, color = TRUE, linetype = 1, opt = NULL )"},{"path":"https://inkaverse.com/reference/plot_raw.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot raw data — plot_raw","text":"data raw data type Type graphic. \"boxplot\" \"scatterplot\" x Axis x variable y Axis y variable group Group variable xlab Title axis x ylab Title axis y glab Title legend ylimits Limits break y axis c(initial, end, brakes) xlimits scatter plot. Limits break x axis c(initial, end, brakes) xrotation Rotation x axis c(angle, h, v) legend position legends (\"none\", \"left\", \"right\", \"bottom\", \"top\", two-element numeric vector) xtext Text labels x axis using vector gtext Text labels groups using vector color Colored figure (TRUE), black & white (FALSE) color vector linetype Line type regression. Default = 0 opt Add new layers plot","code":""},{"path":"https://inkaverse.com/reference/plot_raw.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot raw data — plot_raw","text":"plot","code":""},{"path":"https://inkaverse.com/reference/plot_raw.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Plot raw data — plot_raw","text":"add additional layer plot using \"+\" ggplot2 options","code":""},{"path":"https://inkaverse.com/reference/plot_raw.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot raw data — plot_raw","text":"","code":"if (FALSE) { library(inti) fb <- potato fb %>% plot_raw(type = \"box\" , x = \"geno\" , y = \"twue\" , group = NULL , ylab = NULL , xlab = NULL , glab = \"\" ) fb %>% plot_raw(type = \"sca\" , x = \"geno\" , y = \"twue\" , group = \"treat\" , color = c(\"red\", \"blue\") ) }"},{"path":"https://inkaverse.com/reference/plot_smr.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot summary data — plot_smr","title":"Plot summary data — plot_smr","text":"Graph summary data bar o line plot","code":""},{"path":"https://inkaverse.com/reference/plot_smr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot summary data — plot_smr","text":"","code":"plot_smr( data, type = NULL, x = NULL, y = NULL, group = NULL, xlab = NULL, ylab = NULL, glab = NULL, ylimits = NULL, xrotation = c(0, 0.5, 0.5), xtext = NULL, gtext = NULL, legend = \"top\", sig = NULL, sigsize = 3, error = NULL, color = TRUE, opt = NULL )"},{"path":"https://inkaverse.com/reference/plot_smr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot summary data — plot_smr","text":"data Output summary data type Type graphic. \"bar\" \"line\" x Axis x variable y Axis y variable group Group variable xlab Title axis x ylab Title axis y glab Title legend ylimits limits y axis c(initial, end, brakes) xrotation Rotation x axis c(angle, h, v) xtext Text labels x axis using vector gtext Text labels group using vector legend position legends (\"none\", \"left\", \"right\", \"bottom\", \"top\", two-element numeric vector) sig Column significance sigsize Font size significance letters error Show error bar (\"ste\" \"std\") color colored figure (TRUE), black & white (FALSE) color vector opt Add news layer plot","code":""},{"path":"https://inkaverse.com/reference/plot_smr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot summary data — plot_smr","text":"plot","code":""},{"path":"https://inkaverse.com/reference/plot_smr.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Plot summary data — plot_smr","text":"table put mean_comparison(graph_opts = TRUE) function. contain parameter plot. add additional layer plot using \"+\" ggplot2 options","code":""},{"path":"https://inkaverse.com/reference/plot_smr.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot summary data — plot_smr","text":"","code":"if (FALSE) { library(inti) fb <- potato#' yrs <- yupana_analysis(data = fb , response = \"hi\" , model_factors = \"geno*treat\" , comparison = c(\"geno\", \"treat\") ) yrs$meancomp %>% plot_smr(type = \"line\" , x = \"geno\" , y = \"hi\" , xlab = \"\" , group = \"treat\" , glab = \"Tratamientos\" , ylimits = c(0, 1, 0.2) , color = c(\"red\", \"black\") , gtext = c(\"Irrigado\", \"Sequia\") ) }"},{"path":"https://inkaverse.com/reference/potato.html","id":null,"dir":"Reference","previous_headings":"","what":"Water use efficiency in 15 potato genotypes — potato","title":"Water use efficiency in 15 potato genotypes — potato","text":"Experiment evaluate physiological response 15 potatos genotypes water deficit condition. experiment randomized complete block design five replications. stress started 30 day planting.","code":""},{"path":"https://inkaverse.com/reference/potato.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Water use efficiency in 15 potato genotypes — potato","text":"","code":"potato"},{"path":"https://inkaverse.com/reference/potato.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Water use efficiency in 15 potato genotypes — potato","text":"data frame 150 rows 17 variables: treat Water deficit treatments: sequia, irrigado geno 15 potato genotypes bloque blocks experimentl design spad_29 Relative chlorophyll content (SPAD) 29 day planting spad_83 Relative chlorophyll content (SPAD) 84 day planting rwc_84 Relative water content (percentage) 84 day planting op_84 Osmotic potential (Mpa) 84 day planting leafdw leaf dry weight (g) stemdw stem dry weight (g) rootdw root dry weight (g) tubdw tuber dry weight (g) biomdw total biomass dry weight (g) hi harvest index ttrans total transpiration (l) wue water use effiency (g/l) twue tuber water use effiency (g/l) lfa leaf area (cm2)","code":""},{"path":"https://inkaverse.com/reference/reexports.html","id":null,"dir":"Reference","previous_headings":"","what":"Objects exported from other packages — reexports","title":"Objects exported from other packages — reexports","text":"objects imported packages. Follow links see documentation. dplyr %>%","code":""},{"path":"https://inkaverse.com/reference/split_folder.html","id":null,"dir":"Reference","previous_headings":"","what":"Split folder — split_folder","title":"Split folder — split_folder","text":"Function split folder size number elements","code":""},{"path":"https://inkaverse.com/reference/split_folder.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Split folder — split_folder","text":"","code":"split_folder( folder, export, units = \"megas\", size = 500, zip = TRUE, remove = FALSE )"},{"path":"https://inkaverse.com/reference/split_folder.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Split folder — split_folder","text":"folder Path folder split (path). export Path export split folders (path). units Units split folder (string: \"megas\", \"number\"). size Folder size units selected (numeric). zip Zip split folders (logical). remove Remove split folder zip (logical).","code":""},{"path":"https://inkaverse.com/reference/split_folder.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Split folder — split_folder","text":"zip files","code":""},{"path":"https://inkaverse.com/reference/split_folder.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Split folder — split_folder","text":"","code":"if (FALSE) { split_folder(\"pictures/QUINOA 2018-2019 SC SEEDS EDWIN - CAMACANI/\" , \"pictures/split_num\", remove = T, size = 400, units = \"number\") }"},{"path":"https://inkaverse.com/reference/table2qmd.html","id":null,"dir":"Reference","previous_headings":"","what":"Table to Rmarkdown — table2qmd","title":"Table to Rmarkdown — table2qmd","text":"Use Articul8 Add-ons Google docs build Rticles","code":""},{"path":"https://inkaverse.com/reference/table2qmd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Table to Rmarkdown — table2qmd","text":"","code":"table2qmd(text)"},{"path":"https://inkaverse.com/reference/table2qmd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Table to Rmarkdown — table2qmd","text":"text Markdown text table information (string)","code":""},{"path":"https://inkaverse.com/reference/table2qmd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Table to Rmarkdown — table2qmd","text":"string mutated","code":""},{"path":"https://inkaverse.com/reference/table2rmd.html","id":null,"dir":"Reference","previous_headings":"","what":"Table to Rmarkdown — table2rmd","title":"Table to Rmarkdown — table2rmd","text":"Use Articul8 Add-ons Google docs build Rticles","code":""},{"path":"https://inkaverse.com/reference/table2rmd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Table to Rmarkdown — table2rmd","text":"","code":"table2rmd(text, opts = NA, prefix = \"Table\")"},{"path":"https://inkaverse.com/reference/table2rmd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Table to Rmarkdown — table2rmd","text":"text String table information opts chunk options brackets. prefix Prefix name table","code":""},{"path":"https://inkaverse.com/reference/table2rmd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Table to Rmarkdown — table2rmd","text":"Mutated string","code":""},{"path":"https://inkaverse.com/reference/tarpuy.html","id":null,"dir":"Reference","previous_headings":"","what":"Interactive fieldbook designs — tarpuy","title":"Interactive fieldbook designs — tarpuy","text":"Invoke RStudio addin create fieldbook designs","code":""},{"path":"https://inkaverse.com/reference/tarpuy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Interactive fieldbook designs — tarpuy","text":"","code":"tarpuy(dependencies = FALSE)"},{"path":"https://inkaverse.com/reference/tarpuy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Interactive fieldbook designs — tarpuy","text":"dependencies Install package dependencies run app","code":""},{"path":"https://inkaverse.com/reference/tarpuy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Interactive fieldbook designs — tarpuy","text":"Shiny app","code":""},{"path":"https://inkaverse.com/reference/tarpuy.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Interactive fieldbook designs — tarpuy","text":"Tarpuy allow create experimental designs interactive app.","code":""},{"path":"https://inkaverse.com/reference/tarpuy.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Interactive fieldbook designs — tarpuy","text":"","code":"if(interactive()){ inti::tarpuy() }"},{"path":"https://inkaverse.com/reference/tarpuy_design.html","id":null,"dir":"Reference","previous_headings":"","what":"Fieldbook experimental designs — tarpuy_design","title":"Fieldbook experimental designs — tarpuy_design","text":"Function deploy experimental designs","code":""},{"path":"https://inkaverse.com/reference/tarpuy_design.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fieldbook experimental designs — tarpuy_design","text":"","code":"tarpuy_design( data, nfactors = 1, type = \"crd\", rep = 2, serie = 2, seed = 0, barcode = NA )"},{"path":"https://inkaverse.com/reference/tarpuy_design.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fieldbook experimental designs — tarpuy_design","text":"data Experimental design data frame factors level. See examples. nfactors Number factor experiment(default = 1). See details. type Type experimental arrange (default = \"crd\"). See details. rep Number replications experiment (default = 3). serie Digits plot id (default = 2). seed Replicability draw results (default = 0) always random. See details. barcode Barcode prefix data collection.","code":""},{"path":"https://inkaverse.com/reference/tarpuy_design.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fieldbook experimental designs — tarpuy_design","text":"list fieldbook design","code":""},{"path":"https://inkaverse.com/reference/tarpuy_design.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Fieldbook experimental designs — tarpuy_design","text":"function allows include arguments sheet information design. include 2 columns sheet: {arguments} {values}. See examples. information extracted automatically deploy design. nfactors = 1: crd, rcbd, lsd, lattice. nfactors = 2 (factorial): split-crd, split-rcbd split-lsd nfactors >= 2 (factorial): crd, rcbd, lsd.","code":""},{"path":"https://inkaverse.com/reference/tarpuy_design.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fieldbook experimental designs — tarpuy_design","text":"","code":"if (FALSE) { library(inti) library(gsheet) url <- paste0(\"https://docs.google.com/spreadsheets/d/\" , \"1tDcLZZ9C80uBJK8RRW6_WIyxiL8bIHACStJyf8avuCU/edit#gid=59220990\") # browseURL(url) fb <- gsheet2tbl(url) tarpuy_design(data = fb) }"},{"path":"https://inkaverse.com/reference/tarpuy_plex.html","id":null,"dir":"Reference","previous_headings":"","what":"Fieldbook plan information — tarpuy_plex","title":"Fieldbook plan information — tarpuy_plex","text":"Information build plan experiment (PLEX)","code":""},{"path":"https://inkaverse.com/reference/tarpuy_plex.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fieldbook plan information — tarpuy_plex","text":"","code":"tarpuy_plex( data = NULL, idea = NULL, goal = NULL, hypothesis = NULL, rationale = NULL, objectives = NULL, plan = NULL, institutions = NULL, researchers = NULL, manager = NULL, location = NULL, altitude = NULL, georeferencing = NULL, environment = NULL, start = NA, end = NA, about = NULL, fieldbook = NULL, album = NULL, github = NULL, nfactor = 2, design = \"rcbd\", rep = 3, serie = 2, seed = 0 )"},{"path":"https://inkaverse.com/reference/tarpuy_plex.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fieldbook plan information — tarpuy_plex","text":"data Data fieldbook information. idea idea born. goal main goal project. hypothesis expected results. rationale Based evidence planned experiment. objectives objectives project. plan General description project (M & M). institutions Institutions involved project. researchers Persons involved project. manager Persons responsible collection data. location Location project. altitude Altitude experiment (m..s.l). georeferencing Georeferencing information. environment Environment experiment (greenhouse, lab, etc). start date start experiments. end date end experiments. Short description project. fieldbook Name ID fieldbook/project. album link photos project. github link github repository. nfactor Number factors design. design Type design. rep Number replication. serie Number digits plots. seed Seed randomization.","code":""},{"path":"https://inkaverse.com/reference/tarpuy_plex.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fieldbook plan information — tarpuy_plex","text":"data frame list arguments: info variables design logbook timetable budget","code":""},{"path":"https://inkaverse.com/reference/tarpuy_plex.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Fieldbook plan information — tarpuy_plex","text":"Provide information available.","code":""},{"path":"https://inkaverse.com/reference/tarpuy_plotdesign.html","id":null,"dir":"Reference","previous_headings":"","what":"Fieldbook plot experimental designs — tarpuy_plotdesign","title":"Fieldbook plot experimental designs — tarpuy_plotdesign","text":"Plot fieldbook sketch designs based experimental design","code":""},{"path":"https://inkaverse.com/reference/tarpuy_plotdesign.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fieldbook plot experimental designs — tarpuy_plotdesign","text":"","code":"tarpuy_plotdesign( data, factor, dim = NULL, fill = \"plots\", xlab = NULL, ylab = NULL, glab = NULL )"},{"path":"https://inkaverse.com/reference/tarpuy_plotdesign.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fieldbook plot experimental designs — tarpuy_plotdesign","text":"data Experimental design data frame factors level. See examples. factor Vector name columns factors. dim Dimension reshape design arrangement. fill Value fill experimental units (default = \"plots\"). xlab Title x axis. ylab Title y axis. glab Title group axis.","code":""},{"path":"https://inkaverse.com/reference/tarpuy_plotdesign.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fieldbook plot experimental designs — tarpuy_plotdesign","text":"plot","code":""},{"path":"https://inkaverse.com/reference/tarpuy_plotdesign.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Fieldbook plot experimental designs — tarpuy_plotdesign","text":"function allows plot experimental design according field experiment design.","code":""},{"path":"https://inkaverse.com/reference/tarpuy_varlist.html","id":null,"dir":"Reference","previous_headings":"","what":"Fieldbook variable list — tarpuy_varlist","title":"Fieldbook variable list — tarpuy_varlist","text":"Function include variables evaluate fieldbook design.","code":""},{"path":"https://inkaverse.com/reference/tarpuy_varlist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fieldbook variable list — tarpuy_varlist","text":"","code":"tarpuy_varlist(fieldbook, varlist = NULL)"},{"path":"https://inkaverse.com/reference/tarpuy_varlist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fieldbook variable list — tarpuy_varlist","text":"fieldbook Data frame fieldbook. varlist Data frame variables information. See examples.","code":""},{"path":"https://inkaverse.com/reference/tarpuy_varlist.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fieldbook variable list — tarpuy_varlist","text":"data frame","code":""},{"path":"https://inkaverse.com/reference/tarpuy_varlist.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Fieldbook variable list — tarpuy_varlist","text":"function allows include arguments sheet information variables. include 3 columns sheet: {abbreviation}, {evaluation} {sampling}. See examples. information extracted automatically deploy list variable fieldbook design.","code":""},{"path":"https://inkaverse.com/reference/tarpuy_varlist.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fieldbook variable list — tarpuy_varlist","text":"","code":"if (FALSE) { library(inti) library(gsheet) url <- paste0(\"https://docs.google.com/spreadsheets/d/\" , \"183upHd4wriZz2UnInoo5Ate5YFdk7cZlhE0sMQ2x5iw/edit#gid=532773890\") # browseURL(url) info <- gsheet2tbl(url) fieldbook <- tarpuy_design(data = info) url_var <- paste0(\"https://docs.google.com/spreadsheets/d/\" , \"183upHd4wriZz2UnInoo5Ate5YFdk7cZlhE0sMQ2x5iw/edit#gid=1335288687\") varlist <- gsheet2tbl(url_var) tarpuy_varlist(fieldbook = fieldbook, varlist = varlist) }"},{"path":"https://inkaverse.com/reference/web_table.html","id":null,"dir":"Reference","previous_headings":"","what":"HTML tables for markdown documents — web_table","title":"HTML tables for markdown documents — web_table","text":"Export tables download, pasta copy buttons","code":""},{"path":"https://inkaverse.com/reference/web_table.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"HTML tables for markdown documents — web_table","text":"","code":"web_table( data, caption = NULL, digits = 2, rnames = FALSE, buttons = NULL, file_name = \"file\", scrolly = NULL, columnwidth = \"200px\", width = \"100%\" )"},{"path":"https://inkaverse.com/reference/web_table.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"HTML tables for markdown documents — web_table","text":"data Dataset. caption Title table. digits Digits number table exported. rnames Row names. buttons Buttons: \"excel\", \"copy\" \"none\". Default c(\"excel\", \"copy\") file_name Excel file name scrolly Windows height show table. Default \"45vh\" columnwidth Column width. Default '200px' width Width pixels percentage (Defaults automatic sizing)","code":""},{"path":"https://inkaverse.com/reference/web_table.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"HTML tables for markdown documents — web_table","text":"table markdown format html documents","code":""},{"path":"https://inkaverse.com/reference/web_table.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"HTML tables for markdown documents — web_table","text":"","code":"if (FALSE) { library(inti) met %>% web_table(caption = \"Web table\") }"},{"path":"https://inkaverse.com/reference/yupana.html","id":null,"dir":"Reference","previous_headings":"","what":"Interactive data analysis — yupana","title":"Interactive data analysis — yupana","text":"Invoke RStudio addin analyze graph experimental design data","code":""},{"path":"https://inkaverse.com/reference/yupana.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Interactive data analysis — yupana","text":"","code":"yupana(dependencies = FALSE)"},{"path":"https://inkaverse.com/reference/yupana.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Interactive data analysis — yupana","text":"dependencies Install package dependencies run app","code":""},{"path":"https://inkaverse.com/reference/yupana.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Interactive data analysis — yupana","text":"Shiny app","code":""},{"path":"https://inkaverse.com/reference/yupana.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Interactive data analysis — yupana","text":"Yupana: data analysis graphics experimental designs.","code":""},{"path":"https://inkaverse.com/reference/yupana.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Interactive data analysis — yupana","text":"","code":"if(interactive()){ inti::yupana() }"},{"path":"https://inkaverse.com/reference/yupana_analysis.html","id":null,"dir":"Reference","previous_headings":"","what":"Fieldbook analysis report — yupana_analysis","title":"Fieldbook analysis report — yupana_analysis","text":"Function create complete report fieldbook","code":""},{"path":"https://inkaverse.com/reference/yupana_analysis.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fieldbook analysis report — yupana_analysis","text":"","code":"yupana_analysis( data, last_factor = NULL, response, model_factors, comparison, test_comp = \"SNK\", sig_level = 0.05, plot_dist = \"boxplot\", plot_diag = FALSE, digits = 2 )"},{"path":"https://inkaverse.com/reference/yupana_analysis.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fieldbook analysis report — yupana_analysis","text":"data Field book data. last_factor last factor fieldbook. response Response variable. model_factors Model used experimental design. comparison Factors compare test_comp Comprasison test c(\"SNK\", \"TUKEY\", \"DUNCAN\") sig_level Significal test (default: p = 0.005) plot_dist Plot data distribution (default = \"boxplot\") plot_diag Diagnostic plots model (default = FALSE). digits Digits number table exported.","code":""},{"path":"https://inkaverse.com/reference/yupana_analysis.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fieldbook analysis report — yupana_analysis","text":"list","code":""},{"path":"https://inkaverse.com/reference/yupana_analysis.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fieldbook analysis report — yupana_analysis","text":"","code":"if (FALSE) { library(inti) fb <- potato rsl <- yupana_analysis(data = fb , last_factor = \"bloque\" , response = \"spad_83\" , model_factors = \"geno * treat\" , comparison = c(\"geno\", \"treat\") ) }"},{"path":"https://inkaverse.com/reference/yupana_export.html","id":null,"dir":"Reference","previous_headings":"","what":"Graph options to export — yupana_export","title":"Graph options to export — yupana_export","text":"Function export graph options model parameters","code":""},{"path":"https://inkaverse.com/reference/yupana_export.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Graph options to export — yupana_export","text":"","code":"yupana_export( data, type = NA, xlab = NA, ylab = NA, glab = NA, ylimits = NA, xrotation = c(0, 0.5, 0.5), xtext = NA, gtext = NA, legend = \"top\", sig = NA, error = NA, color = TRUE, opt = NA, dimension = c(20, 10, 100) )"},{"path":"https://inkaverse.com/reference/yupana_export.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Graph options to export — yupana_export","text":"data Result yupana_analysis yupana_import. type Plot type xlab Title axis x ylab Title axis y glab Title legend ylimits limits y axis xrotation Rotation x axis c(angle, h, v) xtext Text labels x axis gtext Text labels group legend position legends (\"none\", \"left\", \"right\", \"bottom\", \"top\", two-element numeric vector) sig Column significance error Show error bar (\"ste\" \"std\"). color colored figure (TRUE), otherwise black & white (FALSE) opt Add news layer plot dimension Dimension graphs","code":""},{"path":"https://inkaverse.com/reference/yupana_export.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Graph options to export — yupana_export","text":"data frame","code":""},{"path":"https://inkaverse.com/reference/yupana_export.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Graph options to export — yupana_export","text":"","code":"if (FALSE) { library(inti) library(gsheet) url <- paste0(\"https://docs.google.com/spreadsheets/d/\" , \"15r7ZwcZZHbEgltlF6gSFvCTFA-CFzVBWwg3mFlRyKPs/edit#gid=172957346\") # browseURL(url) fb <- gsheet2tbl(url) smr <- yupana_analysis(data = fb , last_factor = \"bloque\" , response = \"spad_83\" , model_factors = \"block + geno*treat\" , comparison = c(\"geno\", \"treat\") ) gtab <- yupana_export(smr, type = \"line\", ylimits = c(0, 100, 2)) #> import url <- paste0(\"https://docs.google.com/spreadsheets/d/\" , \"15r7ZwcZZHbEgltlF6gSFvCTFA-CFzVBWwg3mFlRyKPs/edit#gid=1202800640\") # browseURL(url) fb <- gsheet2tbl(url) info <- yupana_import(fb) etab <- yupana_export(info) info2 <- yupana_import(etab) etab2 <- yupana_export(info2) }"},{"path":"https://inkaverse.com/reference/yupana_import.html","id":null,"dir":"Reference","previous_headings":"","what":"Import information from data summary — yupana_import","title":"Import information from data summary — yupana_import","text":"Graph summary data","code":""},{"path":"https://inkaverse.com/reference/yupana_import.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Import information from data summary — yupana_import","text":"","code":"yupana_import(data)"},{"path":"https://inkaverse.com/reference/yupana_import.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Import information from data summary — yupana_import","text":"data Summary information options","code":""},{"path":"https://inkaverse.com/reference/yupana_import.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Import information from data summary — yupana_import","text":"list","code":""},{"path":"https://inkaverse.com/reference/yupana_import.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Import information from data summary — yupana_import","text":"","code":"if (FALSE) { library(inti) library(gsheet) url <- paste0(\"https://docs.google.com/spreadsheets/d/\" , \"15r7ZwcZZHbEgltlF6gSFvCTFA-CFzVBWwg3mFlRyKPs/edit#gid=338518609\") # browseURL(url) fb <- gsheet2tbl(url) info <- yupana_import(fb) }"},{"path":"https://inkaverse.com/reference/yupana_mvr.html","id":null,"dir":"Reference","previous_headings":"","what":"Multivariate Analysis — yupana_mvr","title":"Multivariate Analysis — yupana_mvr","text":"Multivariate analysis PCA HCPC","code":""},{"path":"https://inkaverse.com/reference/yupana_mvr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multivariate Analysis — yupana_mvr","text":"","code":"yupana_mvr( data, last_factor = NULL, summary_by = NULL, groups = NULL, variables = NA )"},{"path":"https://inkaverse.com/reference/yupana_mvr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Multivariate Analysis — yupana_mvr","text":"data Field book data. last_factor last factor fieldbook. summary_by Variables group analysis. groups Groups color PCA. variables Variables use analysis.","code":""},{"path":"https://inkaverse.com/reference/yupana_mvr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Multivariate Analysis — yupana_mvr","text":"result plots","code":""},{"path":"https://inkaverse.com/reference/yupana_mvr.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multivariate Analysis — yupana_mvr","text":"Compute plot information multivariate analysis (PCA, HCPC correlation).","code":""},{"path":"https://inkaverse.com/reference/yupana_reshape.html","id":null,"dir":"Reference","previous_headings":"","what":"Fieldbook reshape — yupana_reshape","title":"Fieldbook reshape — yupana_reshape","text":"Function reshape fieldbook according separation character","code":""},{"path":"https://inkaverse.com/reference/yupana_reshape.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fieldbook reshape — yupana_reshape","text":"","code":"yupana_reshape( data, last_factor, sep, new_colname, from_var = NULL, to_var = NULL, exc_factors = NULL )"},{"path":"https://inkaverse.com/reference/yupana_reshape.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fieldbook reshape — yupana_reshape","text":"data Field book raw data. last_factor last factor field book. sep Character separates last value. new_colname new name column created. from_var first variable case want exclude several. variables. to_var last variable case want exclude several variables. exc_factors Factor exclude reshape.","code":""},{"path":"https://inkaverse.com/reference/yupana_reshape.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fieldbook reshape — yupana_reshape","text":"data frame","code":""},{"path":"https://inkaverse.com/reference/yupana_reshape.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Fieldbook reshape — yupana_reshape","text":"variable name variable_evaluation_rep. reshape function help create column rep new variable name variable_evaluation.","code":""},{"path":"https://inkaverse.com/news/index.html","id":"inti-057","dir":"Changelog","previous_headings":"","what":"inti 0.5.7","title":"inti 0.5.7","text":"figure2rmd() ==> figure2qmd() table2rmd() ==> table2qmd() gdocs2rmd() ==> gdocs2qmd() Fix plot_raw(): “length(x) = 2 > 1’ coercion ’logical(1)” Update jc_tombola() outliers_remove(drop.na = FALSE) allows avoid drop NA values default H2cal() outliers changed NA data.frame yupana_mvr(): update function correlation PCA Yupana: update multivariate analysis","code":""},{"path":"https://inkaverse.com/news/index.html","id":"inti-056","dir":"Changelog","previous_headings":"","what":"inti 0.5.6","title":"inti 0.5.6","text":"CRAN release: 2022-05-19 autoWidth = TRUE columnwidth argument width argument Fix plot_smr(): “length(x) = 2 > 1’ coercion ’logical(1)” New function: split_folder() Yupana: add scale method correlation plot","code":""},{"path":"https://inkaverse.com/news/index.html","id":"inti-055","dir":"Changelog","previous_headings":"","what":"inti 0.5.5","title":"inti 0.5.5","text":"CRAN release: 2022-04-01 Yupana: update yupana_import() using if_any() instead across() Tarpuy: dsg column qr ==> barcode Tarpuy: update sheets names intro section Tarypu: export field-book specific sheet Tarpuy: select sheet field-book sketch Tarpuy: create field-book factor list Tarpuy: column [] design omitted field-book generation CRAN comments: (class(model) == “lmerMod”) => ( (model, “lmerMod”)","code":""},{"path":"https://inkaverse.com/news/index.html","id":"inti-054","dir":"Changelog","previous_headings":"","what":"inti 0.5.4","title":"inti 0.5.4","text":"CRAN release: 2022-02-22 outliers_remove(): change cbind() cbind.data.frame(). Fix apps auth. Thanks Uwe Ligges allow consecutive CRAN updates.","code":""},{"path":"https://inkaverse.com/news/index.html","id":"inti-053","dir":"Changelog","previous_headings":"","what":"inti 0.5.3","title":"inti 0.5.3","text":"CRAN release: 2022-02-18 Complete location name experimental information. Avoid labels axis legend using \"\". Update vignettes using bookdown. Fix table summary H2cal(). Update diagnostic plot plot_diag() lm lmerMod. Update code logIn modules apps. Update correlation graph yupana.","code":""},{"path":"https://inkaverse.com/news/index.html","id":"inti-052","dir":"Changelog","previous_headings":"","what":"inti 0.5.2","title":"inti 0.5.2","text":"CRAN release: 2021-12-19 Fix CRAN comments Fix path install Tarpuy dependencies Include huito logo apps Fix factors Tarpuy field-book export Update code tarpuy_design() Update barcode column split using “_” Update function tarpuy_plex()","code":""},{"path":"https://inkaverse.com/news/index.html","id":"inti-051","dir":"Changelog","previous_headings":"","what":"inti 0.5.1","title":"inti 0.5.1","text":"CRAN release: 2021-12-10 Thanks Jim Holland (@ncsumaize) suggestion improve function. Use Articul8 Add-ons Google docs build Rticles Update pkgdown","code":""},{"path":"https://inkaverse.com/news/index.html","id":"inti-050","dir":"Changelog","previous_headings":"","what":"inti 0.5.0","title":"inti 0.5.0","text":"CRAN release: 2021-11-07 Changes incompatible old versions. Extract information yupana_analysis Import information web yupana_analysis Update function H2cal() Include statistics anova table export results Clean headers export data, exclude “{}” Update load/save interface can exclude: {evaluation} {sampling}","code":""},{"path":"https://inkaverse.com/news/index.html","id":"inti-044","dir":"Changelog","previous_headings":"","what":"inti 0.4.4","title":"inti 0.4.4","text":"CRAN release: 2021-10-01 Update function selection paper meeting Include last_factor selection Function need last_factor Include package version apps Fixed navigation bar apps PCA individual bottom Include version output table Dimension plots multivariate analysis","code":""},{"path":"https://inkaverse.com/news/index.html","id":"inti-043","dir":"Changelog","previous_headings":"","what":"inti 0.4.3","title":"inti 0.4.3","text":"CRAN release: 2021-09-08 Show equation adjusted R scatter plot graph sig include variables summary table plots number reps 1 sig error “none”","code":""},{"path":"https://inkaverse.com/news/index.html","id":"inti-042","dir":"Changelog","previous_headings":"","what":"inti 0.4.2","title":"inti 0.4.2","text":"CRAN release: 2021-08-15 Include info plot_smr() plot_raw Delete legend border Transparent logos background New vignette coding yupana Update Rticles Books template Fix web_table() export xlsx plot_raw() scientific notation labels Include new data set potato Legend position load correct Headers [] excluded analysis Agradecimiento Pedro Barriga por sus sugerencias para mejorar yupana()","code":""},{"path":"https://inkaverse.com/news/index.html","id":"inti-041","dir":"Changelog","previous_headings":"","what":"inti 0.4.1","title":"inti 0.4.1","text":"CRAN release: 2021-06-25 Add significance font size Allows vector colors plots Include “scatter plot” H2cal() include trial option MET New video version > 0.4.1 Add equations regressions plot Include scatter plot “Exploratory” module","code":""},{"path":"https://inkaverse.com/news/index.html","id":"inti-040","dir":"Changelog","previous_headings":"","what":"inti 0.4.0","title":"inti 0.4.0","text":"CRAN release: 2021-05-25 Changes incompatible old versions.","code":""},{"path":"https://inkaverse.com/news/index.html","id":"major-changes-0-4-0","dir":"Changelog","previous_headings":"","what":"Major changes","title":"inti 0.4.0","text":"Deprecated: create_rticles() & rticles() Deprecated shiny app: rticles Rticles Books Vignette explain dependencies use rticles Styled messages New module: Exploratory need fbsm Reactivity analysis Export model information Overwrite graph info Design 3 factor use facet_grid() Allow import/export information plots Reduce font size significance Styled messages Vignette explain modules app Overwrite fieldbook info Box plot graph Can used independently Table create footnotes rename functions Include new logo Vignettes: comparison H2cal() asreml Add data base MET Logo package apps Agradecimiento Khaterine por la idea en el diseño de los logos","code":""},{"path":"https://inkaverse.com/news/index.html","id":"inti-030","dir":"Changelog","previous_headings":"","what":"inti 0.3.0","title":"inti 0.3.0","text":"CRAN release: 2021-04-24 Fix {arguments} xlimits ylimits Update tables style Update template files Vignette describe arguments options Yupana Delete redundant functions info_figure() & info_grahics() Update functions: include_figure() & include_figure() xtext: labels x level gtext: labels group levels","code":""},{"path":"https://inkaverse.com/news/index.html","id":"inti-020","dir":"Changelog","previous_headings":"","what":"inti 0.2.0","title":"inti 0.2.0","text":"CRAN release: 2021-04-14 Changes incompatible old versions.","code":""},{"path":"https://inkaverse.com/news/index.html","id":"major-changes-0-2-0","dir":"Changelog","previous_headings":"","what":"Major changes","title":"inti 0.2.0","text":"Arguments changed syntax fbsm graphics. Delete error messages console run app Change dependency: ggpubr –> cowplot Multivariate analysis need factor levels n>2 Allows copy Statistics table Delete error messages console run app fix dates experiments update code unzip Articul8 files remove treatments column Allows plot 3 factors comparison facet_grid() New arguments plot: xlimits, xrotation, dimension, opt Delete redundant arguments; limits, brakes Suggest use “*” instead “:” Include additional layers plot. e.g. coord_flip() Save plot dimensions exported sheet web_table fix resize table web","code":""},{"path":"https://inkaverse.com/news/index.html","id":"bug-fixes-0-2-0","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"inti 0.2.0","text":"add pkgs.R file load dependencies apps fix auto-install packages inti::tarpuy(T)","code":""},{"path":"https://inkaverse.com/news/index.html","id":"inti-013","dir":"Changelog","previous_headings":"","what":"inti 0.1.3","title":"inti 0.1.3","text":"CRAN release: 2021-03-20","code":""},{"path":"https://inkaverse.com/news/index.html","id":"major-changes-0-1-3","dir":"Changelog","previous_headings":"","what":"Major changes","title":"inti 0.1.3","text":"update bootstrap include code section google auth verification Include QR code fieldbook bslib dependence install CRAN Include video local installation Suppress messages load apps","code":""},{"path":"https://inkaverse.com/news/index.html","id":"inti-012","dir":"Changelog","previous_headings":"","what":"inti 0.1.2","title":"inti 0.1.2","text":"CRAN release: 2020-11-25","code":""},{"path":"https://inkaverse.com/news/index.html","id":"major-changes-0-1-2","dir":"Changelog","previous_headings":"","what":"Major changes","title":"inti 0.1.2","text":"Exclude package multtest depends CRAN error: include_table Search engine web page","code":""},{"path":"https://inkaverse.com/news/index.html","id":"inti-011","dir":"Changelog","previous_headings":"","what":"inti 0.1.1","title":"inti 0.1.1","text":"CRAN release: 2020-11-17","code":""},{"path":"https://inkaverse.com/news/index.html","id":"major-changes-0-1-1","dir":"Changelog","previous_headings":"","what":"Major changes","title":"inti 0.1.1","text":"now apps work locally update bootstrap update packages dependencies apps Graphs: button generate refresh graphs Fieldbook: plot_label fieldbook summary label axis plots Analysis: export analysis sheet name Analysis: round digits export table new functions: info_figure() & info_table() update pkgdown documentation","code":""},{"path":"https://inkaverse.com/news/index.html","id":"bug-fixes-0-1-1","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"inti 0.1.1","text":"fix problem ‘cloud.json’ Multivariate: exclude variables without variation PCA Multivariate: exclude columns NA values Graphs: app stop graph arguments wrong update observeEvent() –> reactive() update app new bookdown release","code":""},{"path":"https://inkaverse.com/news/index.html","id":"inti-010","dir":"Changelog","previous_headings":"","what":"inti 0.1.0","title":"inti 0.1.0","text":"CRAN release: 2020-10-22 First package release","code":""}] diff --git a/inst/yupana/server.R b/inst/yupana/server.R index 63c4c10..b088594 100644 --- a/inst/yupana/server.R +++ b/inst/yupana/server.R @@ -4,7 +4,7 @@ #> open https://flavjack.github.io/inti/ #> open https://flavjack.shinyapps.io/yupanapro/ #> author .: Flavio Lozano-Isla (lozanoisla.com) -#> date .: 2022-04-28 +#> date .: 2022-08-08 # ------------------------------------------------------------------------- # ------------------------------------------------------------------------- @@ -1234,7 +1234,6 @@ output$plot_color <- renderUI({ last_factor <- fieldbook() %>% names() - selectInput(inputId = "mvr_last_factor" , label = "Last Factor" , selected = input$fb_last_factor @@ -1273,7 +1272,6 @@ output$plot_color <- renderUI({ }) - output$mvr_variables <- renderUI({ validate(need(input$mvr_last_factor, "Insert variables")) @@ -1285,35 +1283,24 @@ output$plot_color <- renderUI({ selectInput(inputId = "mvr_variables" , label = "Variables" , multiple = TRUE - , choices = c("all", mvr_variables) - ) - + , choices = c("all" + , mvr_variables) + ) }) # ------------------------------------------------------------------------- mvr <- reactive({ - validate(need(input$mvr_variables, "Choose your variables")) - - n <- fieldbook() %>% - select(input$mvr_factors) %>% - select(!starts_with("{") | !ends_with("}")) %>% - as.list() %>% - map(discard, is.na) %>% - lengths() %>% - prod() - - validate(need(n > 2, "The factors should have more than 2 levels")) + validate(need(input$mvr_variables, "Select your variables")) yupana_mvr( data = fieldbook() , last_factor = input$mvr_last_factor , summary_by = input$mvr_factors , groups = input$mvr_groups - , variables = if(input$mvr_variables == "all") NULL else input$mvr_variables + , variables = input$mvr_variables ) - }) # ------------------------------------------------------------------------- @@ -1333,12 +1320,8 @@ output$plot_color <- renderUI({ outfile <- tempfile(fileext = ".png") png(outfile, width = ancho, height = alto, units = "cm", res = dpi) - - plot.PCA(x = mvr()$pca - , choix = "var" - , autoLab = "auto" - , shadowtext = T - ) %>% print() + + mvr()$plots$pca_var %>% print() graphics.off() @@ -1364,17 +1347,7 @@ output$plot_color <- renderUI({ png(outfile, width = ancho, height = alto, units = "cm", res = dpi) - plot <- plot.PCA(x = mvr()$pca - , choice = "ind" - , habillage = mvr()$param$groups_n - , invisible = "quali" - , autoLab = "auto" - , shadowtext = T - , graph.type = "ggplot" - ) + - theme(legend.position = "bottom") - - plot %>% print() + mvr()$plots$pca_ind %>% print() graphics.off() @@ -1401,7 +1374,9 @@ output$plot_color <- renderUI({ png(outfile, width = ancho, height = alto, units = "cm", res = dpi) - plot.HCPC(x = mvr()$hcpc, choice = "tree") + plot.HCPC(x = mvr()$hcpc + , choice = "tree" + ) graphics.off() @@ -1461,14 +1436,16 @@ output$plot_color <- renderUI({ png(outfile, width = ancho, height = alto, units = "cm", res = dpi, pointsize = 9) - pairs.panels(mvr()$corr$correlation - , hist.col="red" - , pch = 21 - , method = input$mvr_cor_method - , stars = TRUE - , scale = input$mvr_cor_scale - , lm = TRUE - ) + mvr()$data %>% + select(where(is.numeric)) %>% + pairs.panels(x = . + , hist.col="red" + , pch = 21 + , method = input$mvr_cor_method + , stars = TRUE + , scale = input$mvr_cor_scale + , lm = TRUE + ) graphics.off() diff --git a/inst/yupana/ui.R b/inst/yupana/ui.R index 69c740b..ca46c59 100644 --- a/inst/yupana/ui.R +++ b/inst/yupana/ui.R @@ -856,8 +856,8 @@ tabPanel("Graphics", radioButtons(inputId = "mvr_cor_scale" , label = "Correlation scale" - , choices = c("yes" = TRUE - , "no" = FALSE + , choices = c("no" = FALSE + , "yes" = TRUE ) , inline = TRUE ), diff --git a/man/yupana_mvr.Rd b/man/yupana_mvr.Rd index 316d372..b90c051 100644 --- a/man/yupana_mvr.Rd +++ b/man/yupana_mvr.Rd @@ -9,7 +9,7 @@ yupana_mvr( last_factor = NULL, summary_by = NULL, groups = NULL, - variables = NULL + variables = NA ) } \arguments{ @@ -33,27 +33,3 @@ Multivariate analysis for PCA and HCPC Compute and plot information for multivariate analysis (PCA, HCPC and correlation). } -\examples{ - -\dontrun{ - -library(inti) -library(gsheet) - -url <- paste0("https://docs.google.com/spreadsheets/d/" - , "15r7ZwcZZHbEgltlF6gSFvCTFA-CFzVBWwg3mFlRyKPs/edit#gid=172957346") -# browseURL(url) - -fb <- gsheet2tbl(url) - -mv <- yupana_mvr(data = fb - , last_factor = "bloque" - , summary_by = c("geno", "treat") - , groups = NULL - ) - -FactoMineR::plot.PCA(mv$pca, choix = "ind", habillage = mv$param$groups) - -} - -} diff --git a/vignettes/extra/files/fig-01.png b/vignettes/extra/files/fig-01.png index a1b3224..a6ab9a8 100644 Binary files a/vignettes/extra/files/fig-01.png and b/vignettes/extra/files/fig-01.png differ diff --git a/vignettes/extra/files/fig-03.png b/vignettes/extra/files/fig-03.png index ecdface..468a6be 100644 Binary files a/vignettes/extra/files/fig-03.png and b/vignettes/extra/files/fig-03.png differ diff --git a/vignettes/extra/files/plot_cluster_map.png b/vignettes/extra/files/plot_cluster_map.png index 275ef8d..48aee64 100644 Binary files a/vignettes/extra/files/plot_cluster_map.png and b/vignettes/extra/files/plot_cluster_map.png differ diff --git a/vignettes/extra/files/plot_pca_ind.png b/vignettes/extra/files/plot_pca_ind.png index 47bc6c8..484df06 100644 Binary files a/vignettes/extra/files/plot_pca_ind.png and b/vignettes/extra/files/plot_pca_ind.png differ diff --git a/vignettes/extra/files/plot_pca_var.png b/vignettes/extra/files/plot_pca_var.png index fc2db20..dd3e9da 100644 Binary files a/vignettes/extra/files/plot_pca_var.png and b/vignettes/extra/files/plot_pca_var.png differ diff --git a/vignettes/extra/yupana-coding.Rmd b/vignettes/extra/yupana-coding.Rmd index ed15210..35c76ff 100644 --- a/vignettes/extra/yupana-coding.Rmd +++ b/vignettes/extra/yupana-coding.Rmd @@ -57,8 +57,6 @@ wue <- fb %>% , ylimits = c(5, 30, 5) , glab = "Tratamientos" ) - - ``` ## Scatter plot @@ -197,7 +195,8 @@ fig$figure #> Principal component Analysis mv <- fb %>% - yupana_mvr(summary_by = c("geno", "treat") + yupana_mvr(last_factor = "bloque" + , summary_by = c("geno", "treat") , groups = "treat" )