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#' Geometric adaptability index
#' @description
#' `r badge('stable')`
#'
#' Performs a stability analysis based on the geometric mean (GAI), according to
#' the following model (Mohammadi and Amri, 2008):
#' \loadmathjax
#' \mjsdeqn{GAI = \sqrt[E]{{\mathop {\bar Y}\nolimits_1 + \mathop {\bar Y}\nolimits_2 + ... + \mathop {\bar Y}\nolimits_i }}}
#' where \mjseqn{\bar Y_1}, \mjseqn{\bar Y_2}, and \mjseqn{\bar Y_i} are
#' the mean yields of the first, second and *i*-th genotypes across
#' environments, and E is the number of environments
#'
#'
#' @param .data The dataset containing the columns related to Environments,
#' Genotypes, replication/block and response variable(s).
#' @param env The name of the column that contains the levels of the
#' environments.
#' @param gen The name of the column that contains the levels of the genotypes.
#' @param resp The response variable(s). To analyze multiple variables in a
#' single procedure use, for example, `resp = c(var1, var2, var3)`.
#' @param verbose Logical argument. If `verbose = FALSE` the code will run
#' silently.
#' @return An object of class `gai`, which is a list containing the results
#' for each variable used in the argument `resp`. For each variable, a
#' tibble with the following columns is returned.
#' * **GEN** the genotype's code.
#' * **GAI** Geometric adaptability index
#' * **GAI_R** The rank for the GAI value.
#' @md
#' @author Tiago Olivoto \email{tiagoolivoto@@gmail.com}
#' @references
#' Mohammadi, R., & Amri, A. (2008). Comparison of parametric and non-parametric
#' methods for selecting stable and adapted durum wheat genotypes in variable
#' environments. Euphytica, 159(3), 419-432.
#' \doi{10.1007/s10681-007-9600-6}.
#'
#' @export
#' @examples
#' \donttest{
#' library(metan)
#' out <- gai(data_ge2,
#' env = ENV,
#' gen = GEN,
#' resp = c(EH, PH, EL, CD, ED, NKE))
#' }
#'
gai <- function(.data, env, gen, resp, verbose = TRUE) {
factors <-
.data %>%
select({{env}}, {{gen}}) %>%
mutate(across(everything(), as.factor))
vars <-
.data %>%
select({{resp}}, -names(factors)) %>%
select_numeric_cols()
factors %<>% set_names("ENV", "GEN")
listres <- list()
nvar <- ncol(vars)
if (verbose == TRUE) {
pb <- progress(max = nvar, style = 4)
}
for (var in 1:nvar) {
data <- factors %>%
mutate(Y = vars[[var]])
if(has_na(data)){
data <- remove_rows_na(data)
has_text_in_num(data)
}
temp <-
make_mat(data, ENV, GEN, Y) %>%
gmean(na.rm = TRUE) %>%
t() %>%
as.data.frame() %>%
rownames_to_column("GEN") %>%
mutate(rank = rank(-V1)) %>%
set_names("GEN", "GAI", "GAI_R")
if (verbose == TRUE) {
run_progress(pb,
actual = var,
text = paste("Evaluating trait", names(vars[var])))
}
listres[[paste(names(vars[var]))]] <- temp
}
return(structure(listres, class = "gai"))
}
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