05: Análise de trilha para caracteres agronômicos e componentes de rendimento em genótipos de linhaça (Linum usitatissimum, L.)

1 Libraries

To reproduce the examples of this material, the R packages the following packages are needed.

2 Dados

df <- 
  import("data/data_mgidi.csv") |> 
  remove_rows_na() |> 
  filter(epoca  %in% c("E1", "E4")) |> 
  replace_string(epoca, pattern = "E4", replacement = "E2")

3 Multicolinearidade

3.1 Encontrar preditores com baixa colinearidade

df |> 
  group_by(epoca) |> 
  non_collinear_vars(-c(rgpla))
## # A tibble: 12 × 3
##    epoca Parameter        values                                
##    <chr> <chr>            <chr>                                 
##  1 E1    Predictors       8                                     
##  2 E1    VIF              3.082                                 
##  3 E1    Condition Number 10.386                                
##  4 E1    Determinant      0.0789318365                          
##  5 E1    Selected         icc, nr, nc, areac, mmg, ngcap, ap, ac
##  6 E1    Removed          ng, mc                                
##  7 E2    Predictors       8                                     
##  8 E2    VIF              5.66                                  
##  9 E2    Condition Number 24.189                                
## 10 E2    Determinant      0.0139500743                          
## 11 E2    Selected         areac, nr, ngcap, ng, icc, mmg, ac, ap
## 12 E2    Removed          nc, mc

3.2 Colinearidade dentro de cada época

coldiag <- 
  df |> 
  group_by(epoca) |>
  colindiag(-c(rgpla, ng, mc))


## INDICADORES
cold <- gmd(coldiag)
# export(cold, "data/path_results.xlsx", which = "coldiag")      

## VIFS
vifs <- get_model_data(coldiag, "VIF")
# export(vifs, "data/path_results.xlsx", which = "vifs")

4 Trilha


mod_trilha <- 
  df |> 
  group_by(epoca) |> 
  path_coeff(resp = rgpla,
             pred = -c(mc, ng))
## Weak multicollinearity. 
## Condition Number: 10.386
## You will probably have path coefficients close to being unbiased. 
## Weak multicollinearity. 
## Condition Number: 25.933
## You will probably have path coefficients close to being unbiased.

p1 <- plot(mod_trilha$data[[1]])
p2 <- plot(mod_trilha$data[[2]])
arrange_ggplot(p1, p2, ncol = 1, 
               guides = "collect",
               tag_levels = "a")


ggsave("figs/path_coefs.jpg",
       height = 12,
       width = 6)

5 Section info

sessionInfo()
## R version 4.2.2 (2022-10-31 ucrt)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 22621)
## 
## Matrix products: default
## 
## locale:
## [1] LC_COLLATE=Portuguese_Brazil.utf8  LC_CTYPE=Portuguese_Brazil.utf8   
## [3] LC_MONETARY=Portuguese_Brazil.utf8 LC_NUMERIC=C                      
## [5] LC_TIME=Portuguese_Brazil.utf8    
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] metan_1.18.0    lubridate_1.9.2 forcats_1.0.0   stringr_1.5.0  
##  [5] dplyr_1.1.2     purrr_1.0.1     readr_2.1.4     tidyr_1.3.0    
##  [9] tibble_3.2.1    ggplot2_3.4.2   tidyverse_2.0.0 rio_0.5.29     
## 
## loaded via a namespace (and not attached):
##  [1] jsonlite_1.8.7      splines_4.2.2       cellranger_1.1.0   
##  [4] yaml_2.3.7          ggrepel_0.9.3       numDeriv_2016.8-1.1
##  [7] pillar_1.9.0        lattice_0.20-45     glue_1.6.2         
## [10] digest_0.6.33       RColorBrewer_1.1-3  polyclip_1.10-4    
## [13] minqa_1.2.5         colorspace_2.1-0    htmltools_0.5.5    
## [16] Matrix_1.6-0        plyr_1.8.8          pkgconfig_2.0.3    
## [19] haven_2.5.3         patchwork_1.1.2     scales_1.2.1       
## [22] tweenr_2.0.2        openxlsx_4.2.5.2    tzdb_0.4.0         
## [25] lme4_1.1-34         ggforce_0.4.1       timechange_0.2.0   
## [28] generics_0.1.3      farver_2.1.1        withr_2.5.0        
## [31] cli_3.6.1           magrittr_2.0.3      readxl_1.4.3       
## [34] evaluate_0.21       GGally_2.1.2        fansi_1.0.4        
## [37] nlme_3.1-160        MASS_7.3-60         foreign_0.8-83     
## [40] textshaping_0.3.6   tools_4.2.2         data.table_1.14.8  
## [43] hms_1.1.3           lifecycle_1.0.3     munsell_0.5.0      
## [46] zip_2.3.0           compiler_4.2.2      systemfonts_1.0.4  
## [49] rlang_1.1.1         grid_4.2.2          nloptr_2.0.3       
## [52] rstudioapi_0.15.0   htmlwidgets_1.6.2   labeling_0.4.2     
## [55] rmarkdown_2.23      boot_1.3-28         gtable_0.3.3       
## [58] lmerTest_3.1-3      reshape_0.8.9       curl_5.0.1         
## [61] R6_2.5.1            knitr_1.43          fastmap_1.1.1      
## [64] utf8_1.2.3          mathjaxr_1.6-0      ragg_1.2.5         
## [67] stringi_1.7.12      Rcpp_1.0.11         vctrs_0.6.3        
## [70] tidyselect_1.2.0    xfun_0.39