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```
#' Correlation between stability indexes
#' @description
#' `r badge('stable')`
#'
#' Computes the Spearman's rank correlation between the parametric and
#' nonparametric stability indexes computed with the function
#' [ge_stats()].
#'
#' @param x An object of class `ge_stats`.
#' @param stats The statistics to compute the correlation. See the section
#' **Details** for more information.
#' @param plot Plot the heat map with the correlations? Defaults to `TRUE`.
#' @param ... Other arguments to be passed to the function
#' [plot.corr_coef()].
#' @details The argument `stats` is used to chose the statistics to show the
#' ranks. Allowed values are `"all"` (All statistics, default), `"par"`
#' (Parametric statistics), `"nonpar"` (Non-parametric statistics), `"ammi"`
#' (AMMI-based stability statistics), or the following values that can be
#' combined into comma-separated character vector. `"Y"` (Response variable),
#' `"Var"` (Genotype's variance), `"Shukla"` (Shukla's variance), `"Wi_g",
#' "Wi_f", "Wi_u"` (Annichiarrico's genotypic confidence index for all,
#' favorable and unfavorable environments, respectively), `"Ecoval"` (Wricke's
#' ecovalence), `"Sij"` (Deviations from the joint-regression analysis),
#' `"R2"` (R-squared from the joint-regression analysis), `"ASTAB"` (AMMI
#' Based Stability Parameter), `"ASI"` (AMMI Stability Index), `"ASV"`
#' (AMMI-stability value), `"AVAMGE"` (Sum Across Environments of Absolute
#' Value of GEI Modelled by AMMI ), `"Da"` (Annicchiarico's D Parameter
#' values), `"Dz"` (Zhang's D Parameter), `"EV"` (Sums of the Averages of the
#' Squared Eigenvector Values), `"FA"` (Stability Measure Based on Fitted AMMI
#' Model), `"MASV"` (Modified AMMI Stability Value), `"SIPC"` (Sums of the
#' Absolute Value of the IPC Scores), `"Za"` (Absolute Value of the Relative
#' Contribution of IPCs to the Interaction), `"WAAS"` (Weighted average of
#' absolute scores), `"HMGV"` (Harmonic mean of the genotypic value), `"RPGV"`
#' (Relative performance of the genotypic values), `"HMRPGV"` (Harmonic mean
#' of the relative performance of the genotypic values), `"Pi_a", "Pi_f",
#' "Pi_u"` (Superiority indexes for all, favorable and unfavorable
#' environments, respectively), `"Gai"` (Geometric adaptability index), `"S1"`
#' (mean of the absolute rank differences of a genotype over the n
#' environments), `"S2"` (variance among the ranks over the k environments),
#' `"S3"` (sum of the absolute deviations), `"S6"` (relative sum of squares of
#' rank for each genotype), `"N1", "N2", "N3", "N4"` (Thennarasu"s
#' statistics)).
#' @return A list with the data (ranks) correlation, p-values and a heat map showing the
#' correlation coefficients.
#' @export
#' @author Tiago Olivoto \email{tiagoolivoto@@gmail.com}
#'
#' @examples
#' \donttest{
#' library(metan)
#' model <- ge_stats(data_ge, ENV, GEN, REP, GY)
#' a <- corr_stab_ind(model)
#' }
#'
corr_stab_ind <- function(x, stats = "all", plot = TRUE, ...){
all_s <- c("Y", "Var", "Shukla", "Wi_g", "Wi_f", "Wi_u", "Ecoval", "Sij", "R2","ASI", "ASV", "AVAMGE", "DA","DZ","EV","FA","MASI","MASV","SIPC","ZA","WAAS","HMGV", "RPGV", "HMRPGV", "Pi_a", "Pi_f", "Pi_u", "Gai", "S1", "S2", "S3", "S6", "N1", "N2", "N3", "N4")
par_s <- c("Y", "Var", "Shukla", "Wi_g", "Wi_f", "Wi_u", "Ecoval", "Sij", "R2","ASI", "ASV", "AVAMGE", "DA","DZ","EV","FA","MASI","MASV","SIPC","ZA","WAAS","HMGV", "RPGV", "HMRPGV")
nonpar_s <- c("Y", "Pi_a", "Pi_f", "Pi_u", "Gai", "S1", "S2", "S3", "S6", "N1", "N2", "N3", "N4" )
ammi_s <- c("Y", "ASI", "ASV", "AVAMGE", "DA","DZ","EV","FA","MASI","MASV","SIPC","ZA","WAAS")
if(!stats %in% c("all", "par", "nonpar", "ammi")){
stats = unlist(strsplit(stats, split=", "))
} else {
if(any(stats == "all")){
stats = all_s
}
if(any(stats == "par")){
stats = par_s
}
if(any(stats == "nonpar")){
stats = nonpar_s
}
if(any(stats == "ammi")){
stats = ammi_s
}
}
if(any(!stats %in% c("all", "par", "nonpar", "ammi", all_s)) == TRUE){
stop("Argument 'stats' with invalid values. See ?corr_stab_ind for more details.", call. = FALSE)
}
bind <- do.call(
cbind,
lapply(x, function(x) {
x %>% select(contains("_R"))
})) %>%
as_tibble() %>%
mutate(gen = x[[1]][["GEN"]]) %>%
pivot_longer(cols = contains(".")) %>%
separate(name, into = c("var", "stat"), sep = "(\\.)") %>%
separate(stat, into = "stat", sep = "_(?=[^_]*$)", extra = "drop") %>%
pivot_wider(values_from = value, names_from = stat) %>%
select(-c(var, gen))
bind <- select(bind, stats)
corr <- corr_coef(bind)
p <- plot(corr, legend.title = "Speraman's\nCorrelation", ...)
if(plot == TRUE){
suppressWarnings(print(p))
}
invisible(structure(list(data = bind,
corr = corr$cor,
pval = corr$pval,
plot = p),
class = "corr_stab"))
}
```

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