| Title: | Bayesian Stability Analysis of Genotype by Environment Interaction (GEI) |
|---|---|
| Description: | Performs general Bayesian estimation method of linear–bilinear models for genotype × environment interaction. The method is explained in Perez-Elizalde, S., Jarquin, D., and Crossa, J. (2011) (<doi:10.1007/s13253-011-0063-9>). |
| Authors: | Muhammad Yaseen [aut, cre], Diego Jarquin [aut, ctb], Sergio Perez-Elizalde [aut, ctb], Juan Burgueño [aut, ctb], Jose Crossa [aut, ctb] |
| Maintainer: | Muhammad Yaseen <[email protected]> |
| License: | GPL-2 |
| Version: | 0.1.0 |
| Built: | 2025-03-11 03:15:33 UTC |
| Source: | https://github.com/myaseen208/baystability |
Bayesian estimation method of linear–bilinear models for Genotype by Environment Interaction Model
## Default S3 method: bayes_ammi(.data, .y, .gen, .env, .rep, .nIter)## Default S3 method: bayes_ammi(.data, .y, .gen, .env, .rep, .nIter)
.data |
data.frame |
.y |
Response Variable |
.gen |
Genotypes Factor |
.env |
Environment Factor |
.rep |
Replication Factor |
.nIter |
Number of Iterations |
Genotype by Environment Interaction Model
Muhammad Yaseen ([email protected])
Diego Jarquin ([email protected])
Sergio Perez-Elizalde ([email protected])
Juan Burgueño ([email protected])
Jose Crossa ([email protected])
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. Journal of Agricultural, Biological, and Environmental Statistics, 17, 15–37. (doi:10.1007/s13253-011-0063-9)
library(baystability) data(cultivo2008) fm1 <- ge_ammi( .data = cultivo2008 , .y = y , .gen = entry , .env = site , .rep = rep ) r0 <- fm1$g c0 <- fm1$e n0 <- fm1$Rep k0 <- fm1$k mu0 <- fm1$mu sigma20 <- fm1$sigma2 tau0 <- fm1$tau tao0 <- fm1$tao delta0 <- fm1$delta lambdas0 <- fm1$lambdas alphas0 <- fm1$alphas gammas0 <- fm1$gammas ge_means0 <- fm1$ge_means$ge_means data(cultivo2008) fm2 <- ge_ammi( .data = cultivo2009 , .y = y , .gen = entry , .env = site , .rep = rep ) k <- fm2$k alphasa <- fm2$alphas gammasa <- fm2$gammas alphas1 <- tibble::as_tibble(fm2$alphas) gammas1 <- tibble::as_tibble(fm2$gammas) # Biplots OLS library(ggplot2) BiplotOLS1 <- ggplot(data = alphas1, mapping = aes(x = V1, y = V2)) + geom_point() + geom_hline(yintercept = 0) + geom_vline(xintercept = 0) + geom_text(aes(label = 1:nrow(alphas1)), vjust = "inward", hjust = "inward") + scale_x_continuous( limits = c(-max(abs(c(range(alphas1[, 1:2])))) , max(abs(c(range(alphas1[, 1:2])))))) + scale_y_continuous( limits = c(-max(abs(c(range(alphas1[, 1:2])))) , max(abs(c(range(alphas1[, 1:2])))))) + labs(title = "OLS", x = expression(u[1]), y = expression(u[2])) + theme_bw() + theme(plot.title = element_text(hjust = 0.5)) print(BiplotOLS1) BiplotOLS2 <- ggplot(data = gammas1, mapping = aes(x = V1, y = V2)) + geom_point() + geom_hline(yintercept = 0) + geom_vline(xintercept = 0) + geom_text(aes(label = 1:nrow(gammas1)), vjust = "inward", hjust = "inward") + scale_x_continuous( limits = c(-max(abs(c(range(gammas1[, 1:2])))) , max(abs(c(range(gammas1[, 1:2])))))) + scale_y_continuous( limits = c(-max(abs(c(range(gammas1[, 1:2])))) , max(abs(c(range(gammas1[, 1:2])))))) + labs(title = "OLS", x = expression(v[1]), y = expression(v[2])) + theme_bw() + theme(plot.title = element_text(hjust = 0.5)) print(BiplotOLS2) BiplotOLS3 <- ggplot(data = alphas1, mapping = aes(x = V1, y = V2)) + geom_point() + geom_hline(yintercept = 0) + geom_vline(xintercept = 0) + geom_text(aes(label = 1:nrow(alphas1)), vjust = "inward", hjust = "inward") + geom_point(data = gammas1, mapping = aes(x = V1, y = V2)) + geom_segment(data = gammas1, aes(x = 0, y = 0, xend = V1, yend = V2), arrow = arrow(length = unit(0.2, "cm")), alpha = 0.75, color = "red") + geom_text(data = gammas1, aes(x = V1, y = V2, label = paste0("E", 1:nrow(gammasa))) , vjust = "inward", hjust = "inward") + scale_x_continuous( limits = c(-max(abs(c(range(alphas1[, 1:2], gammas1[, 1:2])))) , max(abs(c(range(alphas1[, 1:2], gammas1[, 1:2])))))) + scale_y_continuous( limits = c(-max(abs(c(range(alphas1[, 1:2], gammas1[, 1:2])))) , max(abs(c(range(alphas1[, 1:2], gammas1[, 1:2])))))) + labs(title = "OLS", x = expression(PC[1]), y = expression(PC[2])) + theme_bw() + theme(plot.title = element_text(hjust = 0.5)) print(BiplotOLS3) data(cultivo2009) fm3 <- bayes_ammi( .data = cultivo2009 , .y = y , .gen = entry , .env = site , .rep = rep , .nIter = 200 ) Mean_Alphas <- fm3$Mean_Alphas Mean_Gammas <- fm3$Mean_Gammas # Biplots Bayesian BiplotBayes1 <- ggplot(data = Mean_Alphas, mapping = aes(x = V1, y = V2)) + geom_point() + geom_hline(yintercept = 0) + geom_vline(xintercept = 0) + geom_text(aes(label = 1:nrow(Mean_Alphas)), vjust = "inward" , hjust = "inward") + scale_x_continuous( limits = c(-max(abs(c(range(Mean_Alphas[, 1:2])))) , max(abs(c(range(Mean_Alphas[, 1:2])))))) + scale_y_continuous( limits = c(-max(abs(c(range(Mean_Alphas[, 1:2])))) , max(abs(c(range(Mean_Alphas[, 1:2])))))) + labs(title = "Bayes", x = expression(u[1]), y = expression(u[2])) + theme_bw() + theme(plot.title = element_text(hjust = 0.5)) print(BiplotBayes1) BiplotBayes2 <- ggplot(data = Mean_Gammas, mapping = aes(x = V1, y = V2)) + geom_point() + geom_hline(yintercept = 0) + geom_vline(xintercept = 0) + geom_text(aes(label = 1:nrow(Mean_Gammas)), vjust = "inward", hjust = "inward") + scale_x_continuous( limits = c(-max(abs(c(range(Mean_Gammas[, 1:2])))) , max(abs(c(range(Mean_Gammas[, 1:2])))))) + scale_y_continuous( limits = c(-max(abs(c(range(Mean_Gammas[, 1:2])))) , max(abs(c(range(Mean_Gammas[, 1:2])))))) + labs(title = "Bayes", x = expression(v[1]), y = expression(v[2])) + theme_bw() + theme(plot.title = element_text(hjust = 0.5)) print(BiplotBayes2) BiplotBayes3 <- ggplot(data = Mean_Alphas, mapping = aes(x = V1, y = V2)) + geom_point() + geom_hline(yintercept = 0) + geom_vline(xintercept = 0) + geom_text(aes(label = 1:nrow(Mean_Alphas)), vjust = "inward", hjust = "inward") + geom_point(data = Mean_Gammas, mapping = aes(x = V1, y = V2)) + geom_segment(data = Mean_Gammas, aes(x = 0, y = 0, xend = V1, yend = V2), arrow = arrow(length = unit(0.2, "cm")) , alpha = 0.75, color = "red") + geom_text(data = Mean_Gammas, aes(x = V1, y = V2, label = paste0("E", 1:nrow(Mean_Gammas))), vjust = "inward", hjust = "inward") + scale_x_continuous( limits = c(-max(abs(c(range(Mean_Alphas[, 1:2], Mean_Gammas[, 1:2])))) , max(abs(c(range(Mean_Alphas[, 1:2], Mean_Gammas[, 1:2])))))) + scale_y_continuous( limits = c(-max(abs(c(range(Mean_Alphas[, 1:2], Mean_Gammas[, 1:2])))) , max(abs(c(range(Mean_Alphas[, 1:2], Mean_Gammas[, 1:2])))))) + labs(title = "Bayes", x = expression(PC[1]), y = expression(PC[2])) + theme_bw() + theme(plot.title = element_text(hjust = 0.5)) print(BiplotBayes3) Plot1Mu <- ggplot(data = fm3$mu1, mapping = aes(x = 1:nrow(fm3$mu1), y = mu)) + geom_line(color = "blue") + scale_x_continuous(labels = scales::comma) + scale_y_continuous(labels = scales::comma) + labs(y = expression(mu), x = "Iterations") + theme_bw() print(Plot1Mu) Plot2Mu <- ggplot(data = fm3$mu1, mapping = aes(mu)) + geom_histogram() + scale_x_continuous(labels = scales::comma) + scale_y_continuous(labels = scales::comma) + labs(y = "Frequency", x = expression(mu)) + theme_bw() print(Plot2Mu) Plot1Sigma2 <- ggplot(data = fm3$tau1, mapping = aes(x = 1:nrow(fm3$tau1), y = tau)) + geom_line(color = "blue") + scale_x_continuous(labels = scales::comma) + scale_y_continuous(labels = scales::comma) + labs(y = expression(sigma^2), x = "Iterations") + theme_bw() print(Plot1Sigma2) Plot2Sigma2 <- ggplot(data = fm3$tau1, mapping = aes(tau)) + geom_histogram() + scale_x_continuous(labels = scales::comma) + scale_y_continuous(labels = scales::comma) + labs(y = "Frequency", x = expression(sigma^2)) + theme_bw() print(Plot2Sigma2) # Plot of Alphas Plot1Alpha1 <- ggplot(data = fm3$tao1, mapping = aes(x = 1:nrow(fm3$tao1), y = tao1)) + geom_line(color = "blue") + scale_x_continuous(labels = scales::comma) + scale_y_continuous(labels = scales::comma) + labs(y = expression(alpha[1]), x = "Iterations") + theme_bw() print(Plot1Alpha1) Plot2Alpha1 <- ggplot(data = fm3$tao1, mapping = aes(tao1)) + geom_histogram() + scale_x_continuous(labels = scales::comma) + scale_y_continuous(labels = scales::comma) + labs(y = "Frequency", x = expression(alpha[1])) + theme_bw() print(Plot2Alpha1) Plot1Alpha2 <- ggplot(data = fm3$tao1, mapping = aes(x = 1:nrow(fm3$tao1), y = tao2)) + geom_line(color = "blue") + scale_x_continuous(labels = scales::comma) + scale_y_continuous(labels = scales::comma) + labs(y = expression(alpha[2]), x = "Iterations") + theme_bw() print(Plot1Alpha2) Plot2Alpha2 <- ggplot(data = fm3$tao1, mapping = aes(tao2)) + geom_histogram() + scale_x_continuous(labels = scales::comma) + scale_y_continuous(labels = scales::comma) + labs(y = "Frequency", x = expression(alpha[2])) + theme_bw() print(Plot2Alpha2) # Plot of Betas Plot1Beta1 <- ggplot(data = fm3$delta1, mapping = aes(x = 1:nrow(fm3$delta1), y = delta1)) + geom_line(color = "blue") + scale_x_continuous(labels = scales::comma) + scale_y_continuous(labels = scales::comma) + labs(y = expression(beta[1]), x = "Iterations") + theme_bw() print(Plot1Beta1) Plot2Beta1 <- ggplot(data = fm3$delta1, mapping = aes(delta1)) + geom_histogram() + scale_x_continuous(labels = scales::comma) + scale_y_continuous(labels = scales::comma) + labs(y = "Frequency", x = expression(beta[1])) + theme_bw() print(Plot2Beta1) Plot1Beta2 <- ggplot(data = fm3$delta1, mapping = aes(x = 1:nrow(fm3$delta1), y = delta2)) + geom_line(color = "blue") + scale_x_continuous(labels = scales::comma) + scale_y_continuous(labels = scales::comma) + labs(y = expression(beta[2]), x = "Iterations") + theme_bw() print(Plot1Beta2) Plot2Beta2 <- ggplot(data = fm3$delta1, mapping = aes(delta2)) + geom_histogram() + scale_x_continuous(labels = scales::comma) + scale_y_continuous(labels = scales::comma) + labs(y = "Frequency", x = expression(beta[2])) + theme_bw() print(Plot2Beta2) Plot1Beta3 <- ggplot(data = fm3$delta1, mapping = aes(x = 1:nrow(fm3$delta1), y = delta3)) + geom_line(color = "blue") + scale_x_continuous(labels = scales::comma) + scale_y_continuous(labels = scales::comma) + labs(y = expression(beta[3]), x = "Iterations") + theme_bw() print(Plot1Beta3) Plot2Beta3 <- ggplot(data = fm3$delta1, mapping = aes(delta3)) + geom_histogram() + scale_x_continuous(labels = scales::comma) + scale_y_continuous(labels = scales::comma) + labs(y = "Frequency", x = expression(beta[3])) + theme_bw() print(Plot2Beta3)library(baystability) data(cultivo2008) fm1 <- ge_ammi( .data = cultivo2008 , .y = y , .gen = entry , .env = site , .rep = rep ) r0 <- fm1$g c0 <- fm1$e n0 <- fm1$Rep k0 <- fm1$k mu0 <- fm1$mu sigma20 <- fm1$sigma2 tau0 <- fm1$tau tao0 <- fm1$tao delta0 <- fm1$delta lambdas0 <- fm1$lambdas alphas0 <- fm1$alphas gammas0 <- fm1$gammas ge_means0 <- fm1$ge_means$ge_means data(cultivo2008) fm2 <- ge_ammi( .data = cultivo2009 , .y = y , .gen = entry , .env = site , .rep = rep ) k <- fm2$k alphasa <- fm2$alphas gammasa <- fm2$gammas alphas1 <- tibble::as_tibble(fm2$alphas) gammas1 <- tibble::as_tibble(fm2$gammas) # Biplots OLS library(ggplot2) BiplotOLS1 <- ggplot(data = alphas1, mapping = aes(x = V1, y = V2)) + geom_point() + geom_hline(yintercept = 0) + geom_vline(xintercept = 0) + geom_text(aes(label = 1:nrow(alphas1)), vjust = "inward", hjust = "inward") + scale_x_continuous( limits = c(-max(abs(c(range(alphas1[, 1:2])))) , max(abs(c(range(alphas1[, 1:2])))))) + scale_y_continuous( limits = c(-max(abs(c(range(alphas1[, 1:2])))) , max(abs(c(range(alphas1[, 1:2])))))) + labs(title = "OLS", x = expression(u[1]), y = expression(u[2])) + theme_bw() + theme(plot.title = element_text(hjust = 0.5)) print(BiplotOLS1) BiplotOLS2 <- ggplot(data = gammas1, mapping = aes(x = V1, y = V2)) + geom_point() + geom_hline(yintercept = 0) + geom_vline(xintercept = 0) + geom_text(aes(label = 1:nrow(gammas1)), vjust = "inward", hjust = "inward") + scale_x_continuous( limits = c(-max(abs(c(range(gammas1[, 1:2])))) , max(abs(c(range(gammas1[, 1:2])))))) + scale_y_continuous( limits = c(-max(abs(c(range(gammas1[, 1:2])))) , max(abs(c(range(gammas1[, 1:2])))))) + labs(title = "OLS", x = expression(v[1]), y = expression(v[2])) + theme_bw() + theme(plot.title = element_text(hjust = 0.5)) print(BiplotOLS2) BiplotOLS3 <- ggplot(data = alphas1, mapping = aes(x = V1, y = V2)) + geom_point() + geom_hline(yintercept = 0) + geom_vline(xintercept = 0) + geom_text(aes(label = 1:nrow(alphas1)), vjust = "inward", hjust = "inward") + geom_point(data = gammas1, mapping = aes(x = V1, y = V2)) + geom_segment(data = gammas1, aes(x = 0, y = 0, xend = V1, yend = V2), arrow = arrow(length = unit(0.2, "cm")), alpha = 0.75, color = "red") + geom_text(data = gammas1, aes(x = V1, y = V2, label = paste0("E", 1:nrow(gammasa))) , vjust = "inward", hjust = "inward") + scale_x_continuous( limits = c(-max(abs(c(range(alphas1[, 1:2], gammas1[, 1:2])))) , max(abs(c(range(alphas1[, 1:2], gammas1[, 1:2])))))) + scale_y_continuous( limits = c(-max(abs(c(range(alphas1[, 1:2], gammas1[, 1:2])))) , max(abs(c(range(alphas1[, 1:2], gammas1[, 1:2])))))) + labs(title = "OLS", x = expression(PC[1]), y = expression(PC[2])) + theme_bw() + theme(plot.title = element_text(hjust = 0.5)) print(BiplotOLS3) data(cultivo2009) fm3 <- bayes_ammi( .data = cultivo2009 , .y = y , .gen = entry , .env = site , .rep = rep , .nIter = 200 ) Mean_Alphas <- fm3$Mean_Alphas Mean_Gammas <- fm3$Mean_Gammas # Biplots Bayesian BiplotBayes1 <- ggplot(data = Mean_Alphas, mapping = aes(x = V1, y = V2)) + geom_point() + geom_hline(yintercept = 0) + geom_vline(xintercept = 0) + geom_text(aes(label = 1:nrow(Mean_Alphas)), vjust = "inward" , hjust = "inward") + scale_x_continuous( limits = c(-max(abs(c(range(Mean_Alphas[, 1:2])))) , max(abs(c(range(Mean_Alphas[, 1:2])))))) + scale_y_continuous( limits = c(-max(abs(c(range(Mean_Alphas[, 1:2])))) , max(abs(c(range(Mean_Alphas[, 1:2])))))) + labs(title = "Bayes", x = expression(u[1]), y = expression(u[2])) + theme_bw() + theme(plot.title = element_text(hjust = 0.5)) print(BiplotBayes1) BiplotBayes2 <- ggplot(data = Mean_Gammas, mapping = aes(x = V1, y = V2)) + geom_point() + geom_hline(yintercept = 0) + geom_vline(xintercept = 0) + geom_text(aes(label = 1:nrow(Mean_Gammas)), vjust = "inward", hjust = "inward") + scale_x_continuous( limits = c(-max(abs(c(range(Mean_Gammas[, 1:2])))) , max(abs(c(range(Mean_Gammas[, 1:2])))))) + scale_y_continuous( limits = c(-max(abs(c(range(Mean_Gammas[, 1:2])))) , max(abs(c(range(Mean_Gammas[, 1:2])))))) + labs(title = "Bayes", x = expression(v[1]), y = expression(v[2])) + theme_bw() + theme(plot.title = element_text(hjust = 0.5)) print(BiplotBayes2) BiplotBayes3 <- ggplot(data = Mean_Alphas, mapping = aes(x = V1, y = V2)) + geom_point() + geom_hline(yintercept = 0) + geom_vline(xintercept = 0) + geom_text(aes(label = 1:nrow(Mean_Alphas)), vjust = "inward", hjust = "inward") + geom_point(data = Mean_Gammas, mapping = aes(x = V1, y = V2)) + geom_segment(data = Mean_Gammas, aes(x = 0, y = 0, xend = V1, yend = V2), arrow = arrow(length = unit(0.2, "cm")) , alpha = 0.75, color = "red") + geom_text(data = Mean_Gammas, aes(x = V1, y = V2, label = paste0("E", 1:nrow(Mean_Gammas))), vjust = "inward", hjust = "inward") + scale_x_continuous( limits = c(-max(abs(c(range(Mean_Alphas[, 1:2], Mean_Gammas[, 1:2])))) , max(abs(c(range(Mean_Alphas[, 1:2], Mean_Gammas[, 1:2])))))) + scale_y_continuous( limits = c(-max(abs(c(range(Mean_Alphas[, 1:2], Mean_Gammas[, 1:2])))) , max(abs(c(range(Mean_Alphas[, 1:2], Mean_Gammas[, 1:2])))))) + labs(title = "Bayes", x = expression(PC[1]), y = expression(PC[2])) + theme_bw() + theme(plot.title = element_text(hjust = 0.5)) print(BiplotBayes3) Plot1Mu <- ggplot(data = fm3$mu1, mapping = aes(x = 1:nrow(fm3$mu1), y = mu)) + geom_line(color = "blue") + scale_x_continuous(labels = scales::comma) + scale_y_continuous(labels = scales::comma) + labs(y = expression(mu), x = "Iterations") + theme_bw() print(Plot1Mu) Plot2Mu <- ggplot(data = fm3$mu1, mapping = aes(mu)) + geom_histogram() + scale_x_continuous(labels = scales::comma) + scale_y_continuous(labels = scales::comma) + labs(y = "Frequency", x = expression(mu)) + theme_bw() print(Plot2Mu) Plot1Sigma2 <- ggplot(data = fm3$tau1, mapping = aes(x = 1:nrow(fm3$tau1), y = tau)) + geom_line(color = "blue") + scale_x_continuous(labels = scales::comma) + scale_y_continuous(labels = scales::comma) + labs(y = expression(sigma^2), x = "Iterations") + theme_bw() print(Plot1Sigma2) Plot2Sigma2 <- ggplot(data = fm3$tau1, mapping = aes(tau)) + geom_histogram() + scale_x_continuous(labels = scales::comma) + scale_y_continuous(labels = scales::comma) + labs(y = "Frequency", x = expression(sigma^2)) + theme_bw() print(Plot2Sigma2) # Plot of Alphas Plot1Alpha1 <- ggplot(data = fm3$tao1, mapping = aes(x = 1:nrow(fm3$tao1), y = tao1)) + geom_line(color = "blue") + scale_x_continuous(labels = scales::comma) + scale_y_continuous(labels = scales::comma) + labs(y = expression(alpha[1]), x = "Iterations") + theme_bw() print(Plot1Alpha1) Plot2Alpha1 <- ggplot(data = fm3$tao1, mapping = aes(tao1)) + geom_histogram() + scale_x_continuous(labels = scales::comma) + scale_y_continuous(labels = scales::comma) + labs(y = "Frequency", x = expression(alpha[1])) + theme_bw() print(Plot2Alpha1) Plot1Alpha2 <- ggplot(data = fm3$tao1, mapping = aes(x = 1:nrow(fm3$tao1), y = tao2)) + geom_line(color = "blue") + scale_x_continuous(labels = scales::comma) + scale_y_continuous(labels = scales::comma) + labs(y = expression(alpha[2]), x = "Iterations") + theme_bw() print(Plot1Alpha2) Plot2Alpha2 <- ggplot(data = fm3$tao1, mapping = aes(tao2)) + geom_histogram() + scale_x_continuous(labels = scales::comma) + scale_y_continuous(labels = scales::comma) + labs(y = "Frequency", x = expression(alpha[2])) + theme_bw() print(Plot2Alpha2) # Plot of Betas Plot1Beta1 <- ggplot(data = fm3$delta1, mapping = aes(x = 1:nrow(fm3$delta1), y = delta1)) + geom_line(color = "blue") + scale_x_continuous(labels = scales::comma) + scale_y_continuous(labels = scales::comma) + labs(y = expression(beta[1]), x = "Iterations") + theme_bw() print(Plot1Beta1) Plot2Beta1 <- ggplot(data = fm3$delta1, mapping = aes(delta1)) + geom_histogram() + scale_x_continuous(labels = scales::comma) + scale_y_continuous(labels = scales::comma) + labs(y = "Frequency", x = expression(beta[1])) + theme_bw() print(Plot2Beta1) Plot1Beta2 <- ggplot(data = fm3$delta1, mapping = aes(x = 1:nrow(fm3$delta1), y = delta2)) + geom_line(color = "blue") + scale_x_continuous(labels = scales::comma) + scale_y_continuous(labels = scales::comma) + labs(y = expression(beta[2]), x = "Iterations") + theme_bw() print(Plot1Beta2) Plot2Beta2 <- ggplot(data = fm3$delta1, mapping = aes(delta2)) + geom_histogram() + scale_x_continuous(labels = scales::comma) + scale_y_continuous(labels = scales::comma) + labs(y = "Frequency", x = expression(beta[2])) + theme_bw() print(Plot2Beta2) Plot1Beta3 <- ggplot(data = fm3$delta1, mapping = aes(x = 1:nrow(fm3$delta1), y = delta3)) + geom_line(color = "blue") + scale_x_continuous(labels = scales::comma) + scale_y_continuous(labels = scales::comma) + labs(y = expression(beta[3]), x = "Iterations") + theme_bw() print(Plot1Beta3) Plot2Beta3 <- ggplot(data = fm3$delta1, mapping = aes(delta3)) + geom_histogram() + scale_x_continuous(labels = scales::comma) + scale_y_continuous(labels = scales::comma) + labs(y = "Frequency", x = expression(beta[3])) + theme_bw() print(Plot2Beta3)
cultivo2008 is used for performing Genotypes by Environment Interaction (GEI) Analysis.
data(cultivo2008)data(cultivo2008)
A data.frame 1320 obs. of 6 variables.
Gen Genotype
Institute Institute
Rep Replicate
Block Block
Env Environment
Yield Yield Response
Muhammad Yaseen ([email protected])
Diego Jarquin ([email protected])
Sergio Perez-Elizalde ([email protected])
Juan Burgueño ([email protected])
Jose Crossa ([email protected])
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. Journal of Agricultural, Biological, and Environmental Statistics, 17, 15–37. (doi:10.1007/s13253-011-0063-9)
data(cultivo2008)data(cultivo2008)
cultivo2009 is used for performing Genotypes by Environment Interaction (GEI) Analysis.
data(cultivo2009)data(cultivo2009)
A data.frame 1320 obs. of 6 variables.
Gen Genotype
Institute Institute
Rep Replicate
Block Block
Env Environment
Yield Yield Response
Muhammad Yaseen ([email protected])
Diego Jarquin ([email protected])
Sergio Perez-Elizalde ([email protected])
Juan Burgueño ([email protected])
Jose Crossa ([email protected])
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. Journal of Agricultural, Biological, and Environmental Statistics, 17, 15–37. (doi:10.1007/s13253-011-0063-9)
data(cultivo2009)data(cultivo2009)
Calcuates Environment Effects
## Default S3 method: e_eff(.data, .y, .gen, .env)## Default S3 method: e_eff(.data, .y, .gen, .env)
.data |
data.frame |
.y |
Response Variable |
.gen |
Genotypes Factor |
.env |
Environment Factor |
Environment Effects
Muhammad Yaseen ([email protected])
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. Journal of Agricultural, Biological, and Environmental Statistics, 17, 15–37. (doi:10.1007/s13253-011-0063-9)
data(cultivo2008) e_eff( .data = cultivo2008 , .y = y , .gen = entry , .env = site )data(cultivo2008) e_eff( .data = cultivo2008 , .y = y , .gen = entry , .env = site )
Calcuates Genotype Effects
## Default S3 method: g_eff(.data, .y, .gen, .env)## Default S3 method: g_eff(.data, .y, .gen, .env)
.data |
data.frame |
.y |
Response Variable |
.gen |
Genotypes Factor |
.env |
Environment Factor |
Genotype Effects
Muhammad Yaseen ([email protected])
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. Journal of Agricultural, Biological, and Environmental Statistics, 17, 15–37. (doi:10.1007/s13253-011-0063-9)
data(cultivo2008) g_eff( .data = cultivo2008 , .y = y , .gen = entry , .env = site )data(cultivo2008) g_eff( .data = cultivo2008 , .y = y , .gen = entry , .env = site )
Performs Additive Main Effects and Multiplication Interaction Analysis of Genotype by Environment Interaction Model
ge_ammi(.data, .y, .gen, .env, .rep) ## Default S3 method: ge_ammi(.data, .y, .gen, .env, .rep)ge_ammi(.data, .y, .gen, .env, .rep) ## Default S3 method: ge_ammi(.data, .y, .gen, .env, .rep)
.data |
data.frame |
.y |
Response Variable |
.gen |
Genotypes Factor |
.env |
Environment Factor |
.rep |
Replication Factor |
Genotype by Environment Interaction Model
Muhammad Yaseen ([email protected])
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. Journal of Agricultural, Biological, and Environmental Statistics, 17, 15–37. (doi:10.1007/s13253-011-0063-9)
data(cultivo2008) fm1 <- ge_ammi( .data = cultivo2008 , .y = y , .gen = entry , .env = site , .rep = rep ) data(cultivo2009) fm2 <- ge_ammi( .data = cultivo2009 , .y = y , .gen = entry , .env = site , .rep = rep )data(cultivo2008) fm1 <- ge_ammi( .data = cultivo2008 , .y = y , .gen = entry , .env = site , .rep = rep ) data(cultivo2009) fm2 <- ge_ammi( .data = cultivo2009 , .y = y , .gen = entry , .env = site , .rep = rep )
Calcuates Genotype by Environment Interaction Effects
## Default S3 method: ge_eff(.data, .y, .gen, .env)## Default S3 method: ge_eff(.data, .y, .gen, .env)
.data |
data.frame |
.y |
Response Variable |
.gen |
Genotypes Factor |
.env |
Environment Factor |
Genotype by Environment Interaction Effects
Muhammad Yaseen ([email protected])
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. Journal of Agricultural, Biological, and Environmental Statistics, 17, 15–37. (doi:10.1007/s13253-011-0063-9)
data(cultivo2008) ge_eff( .data = cultivo2008 , .y = y , .gen = entry , .env = site )data(cultivo2008) ge_eff( .data = cultivo2008 , .y = y , .gen = entry , .env = site )
Calcuates Genotype by Environment Interaction Means
## Default S3 method: ge_mean(.data, .y, .gen, .env)## Default S3 method: ge_mean(.data, .y, .gen, .env)
.data |
data.frame |
.y |
Response Variable |
.gen |
Genotypes Factor |
.env |
Environment Factor |
Genotype by Environment Interaction Means
Muhammad Yaseen ([email protected])
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. Journal of Agricultural, Biological, and Environmental Statistics, 17, 15–37. (doi:10.1007/s13253-011-0063-9)
data(cultivo2008) ge_mean( .data = cultivo2008 , .y = y , .gen = entry , .env = site )data(cultivo2008) ge_mean( .data = cultivo2008 , .y = y , .gen = entry , .env = site )
Calcuates Genotype by Environment Interaction Model
ge_model(.data, .y, .gen, .env, .rep) ## Default S3 method: ge_model(.data, .y, .gen, .env, .rep)ge_model(.data, .y, .gen, .env, .rep) ## Default S3 method: ge_model(.data, .y, .gen, .env, .rep)
.data |
data.frame |
.y |
Response Variable |
.gen |
Genotypes Factor |
.env |
Environment Factor |
.rep |
Replication Factor |
Genotype by Environment Interaction Model
Muhammad Yaseen ([email protected])
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. Journal of Agricultural, Biological, and Environmental Statistics, 17, 15–37. (doi:10.1007/s13253-011-0063-9)
data(cultivo2008) fm1 <- ge_model( .data = cultivo2008 , .y = y , .gen = entry , .env = site , .rep = rep )data(cultivo2008) fm1 <- ge_model( .data = cultivo2008 , .y = y , .gen = entry , .env = site , .rep = rep )
Calcuates Genotype by Environment Interaction Variances
## Default S3 method: ge_var(.data, .y, .gen, .env)## Default S3 method: ge_var(.data, .y, .gen, .env)
.data |
data.frame |
.y |
Response Variable |
.gen |
Genotypes Factor |
.env |
Environment Factor |
Genotype by Environment Interaction Variances
Muhammad Yaseen ([email protected])
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. Journal of Agricultural, Biological, and Environmental Statistics, 17, 15–37. (doi:10.1007/s13253-011-0063-9)
data(cultivo2008) ge_var( .data = cultivo2008 , .y = y , .gen = entry , .env = site )data(cultivo2008) ge_var( .data = cultivo2008 , .y = y , .gen = entry , .env = site )
Gives k matrix
matrix_k(n) ## Default S3 method: matrix_k(n)matrix_k(n) ## Default S3 method: matrix_k(n)
n |
Number of columns |
Matrix
Muhammad Yaseen ([email protected])
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. Journal of Agricultural, Biological, and Environmental Statistics, 17, 15–37. (doi:10.1007/s13253-011-0063-9)
Perform Orthogonal Normalization of a matrix
orthnorm(u = NULL, basis = TRUE, norm = TRUE) ## Default S3 method: orthnorm(u = NULL, basis = TRUE, norm = TRUE)orthnorm(u = NULL, basis = TRUE, norm = TRUE) ## Default S3 method: orthnorm(u = NULL, basis = TRUE, norm = TRUE)
u |
Matrix |
basis |
Logical argument by default TRUE |
norm |
Logical argument by default TRUE |
Matrix
Muhammad Yaseen ([email protected])
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. Journal of Agricultural, Biological, and Environmental Statistics, 17, 15–37. (doi:10.1007/s13253-011-0063-9)