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Merge pull request #3 from mymil/model_include_state
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re-include glmm equation now that equatiomatic has been updated
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@Joe-Wasserman
Joe-Wasserman authored Oct 19, 2021
2 parents b87f62b + 803fc45 commit 3e3f19e
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46 changes: 23 additions & 23 deletions README.Rmd
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Expand Up @@ -104,29 +104,29 @@ Estimated excess deaths are available at [`/blob/main/results/united_states_coun

* state (random grouping factor)

<!-- This model can be expressed by the equation: -->

<!-- ```{r equation} -->
<!-- equatiomatic::extract_eq( -->
<!-- lmer_model, -->
<!-- wrap = TRUE, -->
<!-- terms_per_line = 2, -->
<!-- swap_var_names = c( -->
<!-- "total_deaths_per_day" = "Total Deaths per Day", -->
<!-- "population_z" = "County Population (z-score)", -->
<!-- "year_zero" = "Years since 2015", -->
<!-- "quarter" = "Quarter", -->
<!-- "region_code" = "County", -->
<!-- "county_set_code" = "County Set", -->
<!-- "state" = "State" -->
<!-- ) -->
<!-- ) %>% -->
<!-- texPreview::tex_preview( -->
<!-- stem = "plot_equation-1", -->
<!-- fileDir = knitr::opts_chunk$get()$fig.path, -->
<!-- returnType = "html" -->
<!-- ) -->
<!-- ``` -->
This model can be expressed by the equation:

```{r equation, message=FALSE}
equatiomatic::extract_eq(
lmer_model,
wrap = TRUE,
terms_per_line = 2,
swap_var_names = c(
"total_deaths_per_day" = "Total Deaths per Day",
"population_z" = "County Population (z-score)",
"year_zero" = "Years since 2015",
"quarter" = "Quarter",
"region_code" = "County",
"county_set_code" = "County Set",
"state" = "State"
)
) %>%
texPreview::tex_preview(
stem = "plot_equation-1",
fileDir = knitr::opts_chunk$get()$fig.path,
returnType = "html"
)
```

The model object is available at [`/blob/main/results/united_states_county_quarterly_model.RDS`](https://github.com/mymil/covid-19-united-states-county-quarterly-excess-deaths/blob/main/results/united_states_county_quarterly_model.RDS).

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96 changes: 41 additions & 55 deletions README.md
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Expand Up @@ -80,28 +80,9 @@ More precisely, **total deaths per day** was regressed on:

- state (random grouping factor)

<!-- This model can be expressed by the equation: -->
<!-- ```{r equation} -->
<!-- equatiomatic::extract_eq( -->
<!-- lmer_model, -->
<!-- wrap = TRUE, -->
<!-- terms_per_line = 2, -->
<!-- swap_var_names = c( -->
<!-- "total_deaths_per_day" = "Total Deaths per Day", -->
<!-- "population_z" = "County Population (z-score)", -->
<!-- "year_zero" = "Years since 2015", -->
<!-- "quarter" = "Quarter", -->
<!-- "region_code" = "County", -->
<!-- "county_set_code" = "County Set", -->
<!-- "state" = "State" -->
<!-- ) -->
<!-- ) %>% -->
<!-- texPreview::tex_preview( -->
<!-- stem = "plot_equation-1", -->
<!-- fileDir = knitr::opts_chunk$get()$fig.path, -->
<!-- returnType = "html" -->
<!-- ) -->
<!-- ``` -->
This model can be expressed by the equation:

<img src="README_files/equation-1.png" width="702" />

The model object is available at
[`/blob/main/results/united_states_county_quarterly_model.RDS`](https://github.com/mymil/covid-19-united-states-county-quarterly-excess-deaths/blob/main/results/united_states_county_quarterly_model.RDS).
Expand Down Expand Up @@ -166,39 +147,44 @@ sessionInfo()
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] texPreview_1.5 tinytex_0.34 knitr_1.31 equatiomatic_0.3.0
## [5] lubridate_1.7.10 data.table_1.14.0 forcats_0.5.1 stringr_1.4.0
## [9] dplyr_1.0.5 purrr_0.3.4 readr_2.0.2 tidyr_1.1.3
## [13] tibble_3.1.0 ggplot2_3.3.5 tidyverse_1.3.1
## [1] lmerTest_3.1-3 lme4_1.1-26 Matrix_1.3-2
## [4] texPreview_1.5 tinytex_0.34 knitr_1.31
## [7] equatiomatic_0.3.0.9000 lubridate_1.7.10 data.table_1.14.0
## [10] forcats_0.5.1 stringr_1.4.0 dplyr_1.0.5
## [13] purrr_0.3.4 readr_2.0.2 tidyr_1.1.3
## [16] tibble_3.1.0 ggplot2_3.3.5 tidyverse_1.3.1
##
## loaded via a namespace (and not attached):
## [1] nlme_3.1-152 fs_1.5.0 httr_1.4.2
## [4] rprojroot_2.0.2 tools_4.0.4 backports_1.2.1
## [7] utf8_1.2.1 R6_2.5.0 svgPanZoom_0.3.4
## [10] mgcv_1.8-33 DBI_1.1.1 colorspace_2.0-0
## [13] withr_2.4.2 tidyselect_1.1.0 curl_4.3
## [16] compiler_4.0.4 cli_2.5.0 rvest_1.0.0
## [19] xml2_1.3.2 desc_1.3.0 labeling_0.4.2
## [22] scales_1.1.1 digest_0.6.27 minqa_1.2.4
## [25] rmarkdown_2.7 base64enc_0.1-3 pkgconfig_2.0.3
## [28] htmltools_0.5.1.1 lme4_1.1-26 highr_0.8
## [31] dbplyr_2.1.1 fastmap_1.1.0 rlang_0.4.10
## [34] readxl_1.3.1 rstudioapi_0.13 shiny_1.6.0
## [37] farver_2.1.0 generics_0.1.0 jsonlite_1.7.2
## [40] magrittr_2.0.1 Matrix_1.3-2 Rcpp_1.0.7
## [43] munsell_0.5.0 fansi_0.4.2 clipr_0.7.1
## [46] lifecycle_1.0.0 stringi_1.5.3 whisker_0.4
## [49] yaml_2.2.1 MASS_7.3-53 grid_4.0.4
## [52] ggrepel_0.9.1 promises_1.2.0.1 crayon_1.4.1
## [55] details_0.2.1 lattice_0.20-41 haven_2.3.1
## [58] splines_4.0.4 hms_1.0.0 magick_2.7.3
## [61] pillar_1.6.0 boot_1.3-26 reprex_2.0.0
## [64] glue_1.4.2 evaluate_0.14 V8_3.4.0
## [67] modelr_0.1.8 nloptr_1.2.2.2 vctrs_0.3.7
## [70] png_0.1-7 tzdb_0.1.2 httpuv_1.6.0
## [73] cellranger_1.1.0 gtable_0.3.0 rematch2_2.1.2
## [76] assertthat_0.2.1 cachem_1.0.4 xfun_0.26
## [79] mime_0.10 xtable_1.8-4 broom_0.7.6
## [82] later_1.2.0 memoise_2.0.0 statmod_1.4.35
## [85] ellipsis_0.3.1 rdocsyntax_0.4.1.9000 here_1.0.1
## [1] rdocsyntax_0.4.1.9000 minqa_1.2.4 colorspace_2.0-0
## [4] ellipsis_0.3.1 rprojroot_2.0.2 base64enc_0.1-3
## [7] fs_1.5.0 rstudioapi_0.13 farver_2.1.0
## [10] ggrepel_0.9.1 fansi_0.4.2 xml2_1.3.2
## [13] splines_4.0.4 cachem_1.0.4 jsonlite_1.7.2
## [16] nloptr_1.2.2.2 broom_0.7.6 dbplyr_2.1.1
## [19] png_0.1-7 broom.mixed_0.2.6 shiny_1.6.0
## [22] clipr_0.7.1 compiler_4.0.4 httr_1.4.2
## [25] backports_1.2.1 assertthat_0.2.1 fastmap_1.1.0
## [28] cli_3.0.1 svgPanZoom_0.3.4 later_1.2.0
## [31] htmltools_0.5.1.1 tools_4.0.4 coda_0.19-4
## [34] gtable_0.3.0 glue_1.4.2 reshape2_1.4.4
## [37] V8_3.4.0 Rcpp_1.0.7 cellranger_1.1.0
## [40] vctrs_0.3.7 pdftools_2.3.1 nlme_3.1-152
## [43] xfun_0.26 rvest_1.0.0 mime_0.10
## [46] lifecycle_1.0.0 statmod_1.4.35 MASS_7.3-53
## [49] scales_1.1.1 hms_1.0.0 promises_1.2.0.1
## [52] rematch2_2.1.2 TMB_1.7.20 yaml_2.2.1
## [55] curl_4.3 memoise_2.0.0 stringi_1.5.3
## [58] highr_0.8 desc_1.3.0 boot_1.3-26
## [61] rlang_0.4.10 pkgconfig_2.0.3 evaluate_0.14
## [64] lattice_0.20-41 labeling_0.4.2 tidyselect_1.1.0
## [67] here_1.0.1 plyr_1.8.6 magrittr_2.0.1
## [70] R6_2.5.0 magick_2.7.3 generics_0.1.0
## [73] DBI_1.1.1 pillar_1.6.0 haven_2.3.1
## [76] whisker_0.4 withr_2.4.2 mgcv_1.8-33
## [79] modelr_0.1.8 crayon_1.4.1 utf8_1.2.1
## [82] tzdb_0.1.2 rmarkdown_2.7 grid_4.0.4
## [85] readxl_1.3.1 qpdf_1.1 reprex_2.0.0
## [88] digest_0.6.27 xtable_1.8-4 httpuv_1.6.0
## [91] numDeriv_2016.8-1.1 details_0.2.1 munsell_0.5.0
## [94] askpass_1.1
```
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14 changes: 14 additions & 0 deletions README_files/plot_equation-1.tex
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@@ -0,0 +1,14 @@
$$
\begin{aligned}
\operatorname{Total\ Deaths\ per\ Day}_{i} &\sim N \left(\mu, \sigma^2 \right) \\
\mu &=\alpha_{j[i],k[i],l[i]} + \beta_{1}(\operatorname{County\ Population\ (z-score)})\ + \\
&\quad \beta_{2}(\operatorname{Years\ since\ 2015}) + \beta_{3}(\operatorname{Quarter}_{\operatorname{2}})\ + \\
&\quad \beta_{4}(\operatorname{Quarter}_{\operatorname{3}}) + \beta_{5}(\operatorname{Quarter}_{\operatorname{4}}) \\
\alpha_{j} &\sim N \left(\mu_{\alpha_{j}}, \sigma^2_{\alpha_{j}} \right)
\text{, for region\_code j = 1,} \dots \text{,J} \\
\alpha_{k} &\sim N \left(\mu_{\alpha_{k}}, \sigma^2_{\alpha_{k}} \right)
\text{, for county\_set\_code k = 1,} \dots \text{,K} \\
\alpha_{l} &\sim N \left(\mu_{\alpha_{l}}, \sigma^2_{\alpha_{l}} \right)
\text{, for state l = 1,} \dots \text{,L}
\end{aligned}
$$

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