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#' @name ge_model
#' @aliases ge_model
#' @title Genotype by Environment Interaction Model
#' @description Calcuates Genotype by Environment Interaction Model
#'
#' @param .data data.frame
#' @param .y Response Variable
#' @param .gen Genotypes Factor
#' @param .env Environment Factor
#' @param .rep Replication Factor
#'
#' @return Genotype by Environment Interaction Model
#'
#' @author
#' \enumerate{
#' \item Muhammad Yaseen (\email{myaseen208@@gmail.com})
#' }
#'
#' @references
#' Perez-Elizalde, S., Jarquin, D., and Crossa, J. (2011)
#' A General Bayesian Estimation Method of Linear–Bilinear Models
#' Applied to Plant Breeding Trials With Genotype × Environment Interaction.
#' \emph{Journal of Agricultural, Biological, and Environmental Statistics},
#' 17, 15–37. (\href{https://link.springer.com/article/10.1007/s13253-011-0063-9}{doi:10.1007/s13253-011-0063-9})
#'
#' @import tidyverse
#' @import tidyr
#' @import lme4
#' @import rlang
#' @importFrom magrittr %>%
#' @importFrom stats sigma
#'
#' @export
#'
#' @examples
#'
#' data(cultivo2008)
#' fm1 <-
#' ge_model(
#' .data = cultivo2008
#' , .y = y
#' , .gen = entry
#' , .env = site
#' , .rep = rep
#' )
#'
#'
ge_model <- function(.data, .y, .gen, .env, .rep) {
UseMethod("ge_model")
}
#' @export
#' @rdname ge_model
ge_model.default <-
function(.data, .y, .gen, .env, .rep){
.y <- deparse(substitute(.y))
.gen <- deparse(substitute(.gen))
.env <- deparse(substitute(.env))
.rep <- deparse(substitute(.rep))
df1 <- data.frame(
Y = .data[[.y]]
, Env = factor(.data[[.env]])
, Gen = factor(.data[[.gen]])
, Rep = factor(.data[[.rep]])
)
ge_fm <-
lme4::lmer(Y ~ Env + Gen + Env:Gen + (1|Env:Rep), data = df1)
return(ge_fm)
}
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