Nothing
if(getRversion() >= "2.15.1"){
utils::globalVariables(c(
"rASD"
))
}
#' @name stab_asv
#' @aliases stab_asv
#' @title Additive Main Effects and Multiplicative Interacion Stability Value
#' @description Additive ANOVA for Genotypes by Environment Interaction (GEI) model
#'
#' @param .data data.frame
#' @param .y Response Variable
#' @param .rep Replication Factor
#' @param .gen Genotypes Factor
#' @param .env Environment Factor
#'
#' @return Additive ANOVA
#'
#' @author
#' \enumerate{
#' \item Muhammad Yaseen (\email{myaseen208@@gmail.com})
#' \item Kent M. Edkridge (\email{keskridge1@@unl.edu})
#' }
#'
#' @references
#' Singh, R. K. and Chaudhary, B. D. (2004) \emph{Biometrical Methods in Quantitative Genetic Analysis}.
#' New Delhi: Kalyani.
#'
#' @import dplyr
#' @import tibble
#' @importFrom magrittr %>%
#'
#' @export
#'
#' @examples
#' data(ge_data)
#' YieldASV <-
#' stab_asv(
#' .data = ge_data
#' , .y = Yield
#' , .rep = Rep
#' , .gen = Gen
#' , .env = Env
#' )
#' YieldASV
#'
stab_asv <- function(.data, .y, .rep, .gen, .env) {
UseMethod("stab_asv")
}
#' @export
#' @rdname stab_asv
stab_asv.default <-
function(.data, .y, .rep, .gen, .env){
Y <- deparse(substitute(.y))
Rep <- deparse(substitute(.rep))
G <- deparse(substitute(.gen))
E <- deparse(substitute(.env))
g <- length(levels(.data[[G]]))
e <- length(levels(.data[[E]]))
r <- length(levels(.data[[Rep]]))
g_means <-
.data %>%
dplyr::group_by(!!rlang::sym(G)) %>%
dplyr::summarize(Mean = mean(!!rlang::sym(Y)))
ge_means <-
.data %>%
dplyr::group_by(!!rlang::sym(G), !!rlang::sym(E)) %>%
dplyr::summarize(GE.Mean = mean(!!rlang::sym(Y))) %>%
tidyr::spread(key = E, value = GE.Mean)
ge_means1 <- as.matrix(ge_means[, -1])
rownames(ge_means1) <- c(ge_means[, 1])[[1]]
gge_effects <-
sweep(
x = ge_means1
, MARGIN = 2
, STATS = colMeans(ge_means1)
)
ge_effects <-
sweep(
x = gge_effects
, MARGIN = 1
, STATS = rowMeans(gge_effects)
)
SVD <- svd(ge_effects)
PC <- SVD$u %*% diag(sqrt(SVD$d))
dimnames(PC) <- list(row.names(ge_effects), paste0("PC", 1:e))
Lambda <- SVD$d
ASD <-
g_means %>%
dplyr::mutate(
ASD = sqrt((Lambda[1]/Lambda[2])*PC[, 1, drop = FALSE]^2 + PC[, 2, drop = FALSE]^2)
, rMean = min_rank(desc(Mean))
, rASD = min_rank(ASD)
, YSI = rMean + rASD
)
return(list(
ASD = ASD
))
}
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