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README.Rmd
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---
output: github_document
bibliography: ./vignettes/bibliography.bib
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
warning = FALSE,
message = FALSE,
collapse = TRUE,
comment = "",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# agriutilities <img src="man/figures/logo.png" align="right" width="160px"/></a>
<!-- badges: start -->
[](https://CRAN.R-project.org/package=agriutilities)
[](https://lifecycle.r-lib.org/articles/stages.html#stable)
[](https://r-pkg.org/pkg/agriutilities)
[](https://r-pkg.org/pkg/agriutilities)
<!-- badges: end -->
agriutilities is an `R` package designed to make the analysis of
field trials easier and more accessible for everyone working in plant breeding.
It provides a simple and intuitive interface for conducting **single** and
**multi-environmental** trial analysis, with minimal coding required. Whether
you're a beginner or an experienced user, agriutilities will help you quickly
and easily carry out complex analyses with confidence. With built-in functions
for fitting Linear Mixed Models (**LMM**), agriutilities is the ideal choice for
anyone who wants to save time and focus on interpreting their results.
## Installation
### From CRAN
``` r
install.packages("agriutilities")
```
### From GitHub
You can install the development version of agriutilities from
[GitHub](https://github.com/AparicioJohan/agriutilities) with:
``` r
remotes::install_github("AparicioJohan/agriutilities")
```
## Automatic Data Analysis Pipeline
This is a basic example which shows you how to use some of the functions of the
package.
### Identify the Experimental Design
The function `check_design_met` helps us to check the quality of the data and
also to identify the experimental design of the trials. This works as a quality
check or quality control before we fit any model.
```{r}
library(agriutilities)
library(agridat)
data(besag.met)
dat <- besag.met
results <- check_design_met(
data = dat,
genotype = "gen",
trial = "county",
traits = "yield",
rep = "rep",
block = "block",
col = "col",
row = "row"
)
```
```{r, fig.dpi=600}
plot(results, type = "connectivity")
plot(results, type = "missing")
```
Inspecting the output.
```{r}
print(results)
```
### Single Trial Analysis (STA)
The results of the previous function are used in `single_trial_analysis()` to
fit single trial models. This function can fit, Completely Randomized Designs
(**CRD**), Randomized Complete Block Designs (**RCBD**), Resolvable Incomplete
Block Designs (**res-IBD**), Non-Resolvable Row-Column Designs (**Row-Col**)
and Resolvable Row-Column Designs (**res-Row-Col**).
> **NOTE**: It fits models based on the randomization detected.
```{r}
obj <- single_trial_analysis(results, progress = FALSE)
```
Inspecting the output.
```{r}
print(obj)
```
```{r, fig.dpi=600}
plot(obj, horizontal = TRUE, nudge_y_h2 = 0.12)
plot(obj, type = "correlation")
```
The returning object is a set of lists with trial summary, BLUEs, BLUPs,
heritability, variance components, potential extreme observations, residuals,
the models fitted and the data used.
### Two-Stage Analysis (MET)
The results of the previous function are used in `met_analysis()` to
fit multi-environmental trial models.
```{r, message=FALSE, warning=FALSE}
met_results <- met_analysis(obj, vcov = "fa2", progress = FALSE)
```
Inspecting the output.
```{r}
print(met_results)
```
### Exploring Factor Analytic in MET analysis.
```{r}
pvals <- met_results$trial_effects
model <- met_results$met_models$yield
fa_objt <- fa_summary(
model = model,
trial = "trial",
genotype = "genotype",
BLUEs_trial = pvals,
k_biplot = 8,
size_label_var = 4,
filter_score = 1
)
```
```{r, fig.dpi=600}
fa_objt$plots$loadings_c
fa_objt$plots$biplot
```
For more information and to learn more about what is described here you may find
useful the following sources: @isik2017genetic; @rodriguez2018correcting.
## Code of Conduct
Please note that the agriutilities project is released with a [Contributor Code
of Conduct](https://apariciojohan.github.io/agriutilities/CODE_OF_CONDUCT.html).
By contributing to this project, you agree to abide by its terms.
# References