Bayesian Estimation of the Additive Main Effects and Multiplicative Interaction Model
Performs Bayesian Estimation of the Additive Main Effects and Multiplicative Interaction Model
## 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 IterationsGenotype by Environment Interaction Model
data(Maiz) fm1 <- bayes_ammi( .data = Maiz , .y = y , .gen = entry , .env = site , .rep = rep , .nIter = 20 ) names(fm1) fm1$mu1 fm1$tau1 fm1$tao1 fm1$delta1 fm1$lambdas1 fm1$alphas1 fm1$gammas1 library(ggplot2) Plot1Mu <- ggplot(data = fm1$mu1, mapping = aes(x = 1:nrow(fm1$mu1), y = mu)) + geom_line(color = "blue") + scale_x_continuous(labels = scales::comma) + scale_y_continuous(labels = scales::comma) + labs(y = expression(mu), x = "Iterations") + theme_bw() print(Plot1Mu) Plot2Mu <- ggplot(data = fm1$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 = fm1$tau1, mapping = aes(x = 1:nrow(fm1$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 = fm1$tau1, mapping = aes(tau)) + geom_histogram() + scale_x_continuous(labels = scales::comma) + scale_y_continuous(labels = scales::comma) + labs(y = "Frequency", x = expression(sigma^2)) + theme_bw() print(Plot2Sigma2) # Plot of Alphas Plot1Alpha1 <- ggplot(data = fm1$tao1, mapping = aes(x = 1:nrow(fm1$tao1), y = tao1)) + geom_line(color = "blue") + scale_x_continuous(labels = scales::comma) + scale_y_continuous(labels = scales::comma) + labs(y = expression(alpha[1]), x = "Iterations") + theme_bw() print(Plot1Alpha1) Plot2Alpha1 <- ggplot(data = fm1$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 = fm1$tao1, mapping = aes(x = 1:nrow(fm1$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 = fm1$tao1, mapping = aes(tao2)) + geom_histogram() + scale_x_continuous(labels = scales::comma) + scale_y_continuous(labels = scales::comma) + labs(y = "Frequency", x = expression(alpha[2])) + theme_bw() print(Plot2Alpha2) # Plot of Betas Plot1Beta1 <- ggplot(data = fm1$delta1, mapping = aes(x = 1:nrow(fm1$delta1), y = delta1)) + geom_line(color = "blue") + scale_x_continuous(labels = scales::comma) + scale_y_continuous(labels = scales::comma) + labs(y = expression(beta[1]), x = "Iterations") + theme_bw() print(Plot1Beta1) Plot2Beta1 <- ggplot(data = fm1$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 = fm1$delta1, mapping = aes(x = 1:nrow(fm1$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 = fm1$delta1, mapping = aes(delta2)) + geom_histogram() + scale_x_continuous(labels = scales::comma) + scale_y_continuous(labels = scales::comma) + labs(y = "Frequency", x = expression(beta[2])) + theme_bw() print(Plot2Beta2) Plot1Beta3 <- ggplot(data = fm1$delta1, mapping = aes(x = 1:nrow(fm1$delta1), y = delta3)) + geom_line(color = "blue") + scale_x_continuous(labels = scales::comma) + scale_y_continuous(labels = scales::comma) + labs(y = expression(beta[3]), x = "Iterations") + theme_bw() print(Plot1Beta3) Plot2Beta3 <- ggplot(data = fm1$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) BiplotAMMI <- ggplot(data = fm1$alphas0, mapping = aes(x = alphas1, y = alphas2)) + geom_point() + geom_hline(yintercept = 0) + geom_vline(xintercept = 0) + geom_text(aes(label = 1:nrow(fm1$alphas0)), vjust = "inward", hjust = "inward") + geom_point(data = fm1$gammas0, mapping = aes(x = gammas1, y = gammas2)) + geom_segment(data = fm1$gammas0, aes(x = 0, y = 0, xend = gammas1, yend = gammas2), arrow = arrow(length = unit(0.2, "cm")) , alpha = 0.75, color = "red") + geom_text(data = fm1$gammas0, aes(x = gammas1, y = gammas2, label = paste0("E", 1:nrow(fm1$gammas0))), vjust = "inward", hjust = "inward") + scale_x_continuous( limits = c(-max(abs(c(range(fm1$alphas0[, 1:2], fm1$gammas0[, 1:2])))) , max(abs(c(range(fm1$alphas0[, 1:2], fm1$gammas0[, 1:2])))))) + scale_y_continuous( limits = c(-max(abs(c(range(fm1$alphas0[, 1:2], fm1$gammas0[, 1:2])))) , max(abs(c(range(fm1$alphas0[, 1:2], fm1$gammas0[, 1:2])))))) + labs(title = "MCO Method", x = expression(PC[1]), y = expression(PC[2])) + theme_bw() + theme(plot.title = element_text(hjust = 0.5)) print(BiplotAMMI) BiplotBayesAMMI <- ggplot(data = fm1$alphas1, mapping = aes(x = alphas1, y = alphas2)) + geom_point() + geom_hline(yintercept = 0) + geom_vline(xintercept = 0) + geom_text(aes(label = 1:nrow(fm1$alphas1)), vjust = "inward", hjust = "inward") + geom_point(data = fm1$gammas1, mapping = aes(x = gammas1, y = gammas2)) + geom_segment(data = fm1$gammas1, aes(x = 0, y = 0, xend = gammas1, yend = gammas2), arrow = arrow(length = unit(0.2, "cm")) , alpha = 0.75, color = "red") + geom_text(data = fm1$gammas1, aes(x = gammas1, y = gammas2, label = paste0("E", 1:nrow(fm1$gammas1))), vjust = "inward", hjust = "inward") + scale_x_continuous( limits = c(-max(abs(c(range(fm1$alphas1[, 1:2], fm1$gammas1[, 1:2])))) , max(abs(c(range(fm1$alphas1[, 1:2], fm1$gammas1[, 1:2])))))) + scale_y_continuous( limits = c(-max(abs(c(range(fm1$alphas1[, 1:2], fm1$gammas1[, 1:2])))) , max(abs(c(range(fm1$alphas1[, 1:2], fm1$gammas1[, 1:2])))))) + labs(title = "Bayesian Method", x = expression(PC[1]), y = expression(PC[2])) + theme_bw() + theme(plot.title = element_text(hjust = 0.5)) print(BiplotBayesAMMI)
Crossa, J., Perez-Elizalde, S., Jarquin, D., Cotes, J.M., Viele, K., Liu, G., and Cornelius, P.L. (2011) Bayesian Estimation of the Additive Main Effects and Multiplicative Interaction Model Crop Science, 51 , 1458–1469. (doi: 10.2135/cropsci2010.06.0343)
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