| Type: | Package |
| Title: | Bayesian Estimation of the Additive Main Effects and Multiplicative Interaction Model |
| Version: | 0.3.0 |
| Maintainer: | Muhammad Yaseen <myaseen208@gmail.com> |
| Description: | Performs Bayesian estimation of the additive main effects and multiplicative interaction (AMMI) model. The method is explained in Crossa, J., Perez-Elizalde, S., Jarquin, D., Cotes, J.M., Viele, K., Liu, G. and Cornelius, P.L. (2011) (<doi:10.2135/cropsci2010.06.0343>). |
| Depends: | R (≥ 3.1) |
| Imports: | dplyr, ggplot2, ggrepel, ks, lme4, magrittr, MASS, mvtnorm, purrr, rlang, rstiefel, scales, stats, tibble, tidyr, tmvtnorm |
| License: | GPL-2 |
| Encoding: | UTF-8 |
| LazyData: | true |
| RoxygenNote: | 7.3.2 |
| Note: | 1. School of Mathematical & Statistical Sciences, Clemson University, Clemson, South Carolina, USA 2. Department of Mathematics & Statistics, University of Agriculture Faisalabad, Faisalabad, Pakistan |
| NeedsCompilation: | no |
| Packaged: | 2024-11-22 17:58:23 UTC; myaseen208 |
| Author: | Muhammad Yaseen [aut, cre], Jose Crossa [aut, ctb], Sergio Perez-Elizalde [aut, ctb], Diego Jarquin [aut, ctb], Jose Miguel Cotes [aut, ctb], Kert Viele [aut, ctb], Genzhou Liu [aut, ctb], Paul L. Cornelius [aut, ctb], Julian Garcia Abadillo Velasco [aut, ctb] |
| Repository: | CRAN |
| Date/Publication: | 2024-11-22 18:10:02 UTC |
Data for Genotypes by Environment Interaction (GEI)
Description
Maiz is used for performing Genotypes by Environment Interaction (GEI) Analysis.
Usage
data(Maiz)
Format
A data.frame 1320 obs. of 6 variables.
Details
Gen Genotype
Institute Institute
Rep Replicate
Block Block
Env Environment
Yield Yield Response
Author(s)
Muhammad Yaseen (myaseen208@gmail.com)
Jose Crossa (j.crossa@cgiar.org)
Sergio Perez-Elizalde (sergiop@colpos.mx)
Diego Jarquin (diego.jarquin@gmail.com)
Jose Miguel Cotes
Kert Viele
Genzhou Liu
Paul L. Cornelius
References
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)
Examples
data(Maiz)
Bayesian Estimation of the Additive Main Effects and Multiplicative Interaction Model
Description
Performs Bayesian Estimation of the Additive Main Effects and Multiplicative Interaction Model
Usage
## Default S3 method:
bayes_ammi(.data, .y, .gen, .env, .rep, .nIter)
Arguments
.data |
data.frame |
.y |
Response Variable |
.gen |
Genotypes Factor |
.env |
Environment Factor |
.rep |
Replication Factor |
.nIter |
Number of Iterations |
Value
Genotype by Environment Interaction Model
Author(s)
Muhammad Yaseen (myaseen208@gmail.com)
Jose Crossa (j.crossa@cgiar.org)
Sergio Perez-Elizalde (sergiop@colpos.mx)
Diego Jarquin (diego.jarquin@gmail.com)
Jose Miguel Cotes
Kert Viele
Genzhou Liu
Paul L. Cornelius
References
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)
Examples
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)
Biplots
Description
biplots
Usage
## Default S3 method:
biplots(
model,
burnin = 0.3,
thin = 0.2,
pb = 0.05,
plot_stable = TRUE,
plot_unstable = TRUE,
ncolors = 5
)
Arguments
model |
Output from 'bayes_ammi()'. This should contain the results of the Bayesian AMMI model, including all sampled iterations. |
burnin |
Numeric. Percentage of iterations to discard as burn-in to avoid the effects of random initializations during sampling. For example, 'burnin = 0.1' removes the first 10% of iterations. |
thin |
Numeric. Proportion of sampled iterations to retain for analysis. For example, 'thin = 0.2' keeps 20% of the iterations, selecting 1 out of every 5 iterations. |
pb |
Numeric. Significance levels for the contours in the plot. Smaller values of 'pb' result in wider contours, while higher values create smaller, more specific contours. |
plot_stable |
Logical. If 'TRUE', stable instances are highlighted in the output plot. |
plot_unstable |
Logical. If 'TRUE', unstable instances are highlighted in the output plot. |
ncolors |
Integer. Specifies the number of distinct colors to use in the plot. Adjust this to control the visual differentiation of elements in the plot. |
Value
A list with the following components:
- plot
A plot displaying the contours and final biplot values.
- contour_data
A 'data.frame' containing the data used to create the contours.
- biplot_data
A 'data.frame' containing the data used to recreate the final biplot values.
Author(s)
Julian Garcia Abadillo Velasco (garciaabadillo.j@ufl.edu)
Diego Jarquin (diego.jarquin@gmail.com)
References
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)
Examples
## Not run:
data(Maiz)
fm1 <-
bayes_ammi(
.data = Maiz,
.y = y,
.gen = entry,
.env = site,
.rep = rep,
.nIter = 200
)
library(ggplot2)
output_05 <- biplots(model = fm1, plot_stable = TRUE, plot_unstable = TRUE, pb = 0.05)
output_05
output_95 <- biplots(model = fm1, plot_stable = TRUE, plot_unstable = TRUE, pb = 0.95)
output_95
## End(Not run)
Environment Effects
Description
Calcuates Environment Effects
Usage
## Default S3 method:
e_eff(.data, .y, .gen, .env)
Arguments
.data |
data.frame |
.y |
Response Variable |
.gen |
Genotypes Factor |
.env |
Environment Factor |
Value
Environment Effects
Author(s)
Muhammad Yaseen (myaseen208@gmail.com)
References
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)
Examples
data(Maiz)
e_eff(
.data = Maiz
, .y = y
, .gen = entry
, .env = site
)
Genotype Effects
Description
Calcuates Genotype Effects
Usage
## Default S3 method:
g_eff(.data, .y, .gen, .env)
Arguments
.data |
data.frame |
.y |
Response Variable |
.gen |
Genotypes Factor |
.env |
Environment Factor |
Value
Genotype Effects
Author(s)
Muhammad Yaseen (myaseen208@gmail.com)
References
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)
Examples
data(Maiz)
g_eff(
.data = Maiz
, .y = y
, .gen = entry
, .env = site
)
AMMI of Genotype by Environment Interaction Model
Description
Performs Additive Main Effects and Multiplication Interaction Analysis of Genotype by Environment Interaction Model
Usage
ge_ammi(.data, .y, .gen, .env, .rep)
## Default S3 method:
ge_ammi(.data, .y, .gen, .env, .rep)
Arguments
.data |
data.frame |
.y |
Response Variable |
.gen |
Genotypes Factor |
.env |
Environment Factor |
.rep |
Replication Factor |
Value
Genotype by Environment Interaction Model
Author(s)
Muhammad Yaseen (myaseen208@gmail.com)
References
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)
Examples
data(Maiz)
fm1 <-
ge_ammi(
.data = Maiz
, .y = y
, .gen = entry
, .env = site
, .rep = rep
)
Genotype by Environment Interaction Effects
Description
Calcuates Genotype by Environment Interaction Effects
Usage
## Default S3 method:
ge_eff(.data, .y, .gen, .env)
Arguments
.data |
data.frame |
.y |
Response Variable |
.gen |
Genotypes Factor |
.env |
Environment Factor |
Value
Genotype by Environment Interaction Effects
Author(s)
Muhammad Yaseen (myaseen208@gmail.com)
References
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)
Examples
data(Maiz)
ge_eff(
.data = Maiz
, .y = y
, .gen = entry
, .env = site
)
Genotype by Environment Interaction Means
Description
Calcuates Genotype by Environment Interaction Means
Usage
## Default S3 method:
ge_mean(.data, .y, .gen, .env)
Arguments
.data |
data.frame |
.y |
Response Variable |
.gen |
Genotypes Factor |
.env |
Environment Factor |
Value
Genotype by Environment Interaction Means
Author(s)
Muhammad Yaseen (myaseen208@gmail.com)
References
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)
Examples
data(Maiz)
ge_mean(
.data = Maiz
, .y = y
, .gen = entry
, .env = site
)
Genotype by Environment Interaction Model
Description
Calcuates Genotype by Environment Interaction Model
Usage
ge_model(.data, .y, .gen, .env, .rep)
## Default S3 method:
ge_model(.data, .y, .gen, .env, .rep)
Arguments
.data |
data.frame |
.y |
Response Variable |
.gen |
Genotypes Factor |
.env |
Environment Factor |
.rep |
Replication Factor |
Value
Genotype by Environment Interaction Model
Author(s)
Muhammad Yaseen (myaseen208@gmail.com)
References
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)
Examples
data(Maiz)
fm1 <-
ge_model(
.data = Maiz
, .y = y
, .gen = entry
, .env = site
, .rep = rep
)
Genotype by Environment Interaction Variances
Description
Calcuates Genotype by Environment Interaction Variances
Usage
## Default S3 method:
ge_var(.data, .y, .gen, .env)
Arguments
.data |
data.frame |
.y |
Response Variable |
.gen |
Genotypes Factor |
.env |
Environment Factor |
Value
Genotype by Environment Interaction Variances
Author(s)
Muhammad Yaseen (myaseen208@gmail.com)
References
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)
Examples
data(Maiz)
ge_var(
.data = Maiz
, .y = y
, .gen = entry
, .env = site
)
k Matrix
Description
Gives k matrix
Usage
matrix_k(n)
## Default S3 method:
matrix_k(n)
Arguments
n |
Number of columns |
Value
Matrix
Author(s)
Muhammad Yaseen (myaseen208@gmail.com)
References
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)
Orthogonal Normalization
Description
Perform Orthogonal Normalization of a matrix
Usage
orthnorm(u = NULL, basis = TRUE, norm = TRUE)
## Default S3 method:
orthnorm(u = NULL, basis = TRUE, norm = TRUE)
Arguments
u |
Matrix |
basis |
Logical argument by default TRUE |
norm |
Logical argument by default TRUE |
Value
Matrix
Author(s)
Muhammad Yaseen (myaseen208@gmail.com)
References
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)