Title: | Response from Genotype to Environment |
---|---|
Description: | Compute yield-stability index based on Bayesian methodology, which is useful for analyze multi-environment trials in plant breeding programs. References: Cotes Torres JM, Gonzalez Jaimes EP, and Cotes Torres A (2016) <https://revistas.unimilitar.edu.co/index.php/rfcb/article/view/2037> Seleccion de Genotipos con Alta Respuesta y Estabilidad Fenotipica en Pruebas Regionales: Recuperando el Concepto Biologico. |
Authors: | Jose Miguel Cotes Torres. Universidad Nacional de Colombia - Sede Medellin |
Maintainer: | Jose Miguel Cotes Torres <[email protected]> |
License: | GPL (>= 2) |
Version: | 1.0 |
Built: | 2024-12-18 06:51:39 UTC |
Source: | CRAN |
RGE is a packages for analize regionals trials from plant breeding programs. The package simplify the analysis process in order to obtaind the more useful results to be consider for the resercher. The program perfomace a GIBBS sampler and finally obtain a bayesian yield stability index. Tools for obtain useful plot were developed in order to make the interpretation of results more easy.
Jose Miguel Cotes Torres [email protected]
Cotes Torres, J. M., Gonzalez Jaimes, E. P., & Cotes Torres, A. (2016). Seleccion de Genotipos con Alta Respuesta y Estabilidad Fenotipica en Pruebas Regionales: Recuperando el Concepto Biologico. Revista Facultad De Ciencias Basicas, 8(2), 226-243.
Obtaining the bayes estimative and the highest posterior density intervals at 95% from a object obtained by RGE function.
bayes.posterior(x, ...)
bayes.posterior(x, ...)
x |
An object obtained by the function RGE |
... |
Further arguments to be passed |
Dataframe with the summary of the posterior distribution
coda package is needed.
Jose Miguel Cotes Torres [email protected]
coda
data(m1) bayes.posterior(m1)
data(m1) bayes.posterior(m1)
Eleven trials with ten genotypes stablished in state of Narino-Colombia Cortesy: Professor Luis Ernesto Rodriguez Molano [email protected]
data("datos")
data("datos")
A data frame with 440 observations on the following 17 variables.
Localidad
a numeric vector
Nlocalidad
a character vector
Semestre
a numeric vector
Bloque
a numeric vector
Genotipo
a character vector
RO
a numeric vector
R1
a numeric vector
Rcomercial
a numeric vector
RcTon
a numeric vector
R2
a numeric vector
R3
a numeric vector
RendimientoTotal
a numeric vector
RTton
a numeric vector
GE
a numeric vector
MS
a numeric vector
CF
a numeric vector
AR
a numeric vector
Unpublished data.
data(datos)
data(datos)
Object obtained by function RGE
data("m1")
data("m1")
The format is: num [1:20, 1:10000] 14.2 15.8 16.1 19.9 17.3 ... - attr(*, "dimnames")=List of 2 ..$ : chr [1:20] "Predicted_T1" "Predicted_T2" "Predicted_UN 4" "Predicted_UN 50" ... ..$ : NULL
data(m1)
data(m1)
This functions makes the plots of bayes estimate (mean) and the highest posterior density intervals at 95%, of predicted value of genotype, his stability variance, and his bayesian yield stability index.
## S3 method for class 'RGE' plot( x, labelg = "Predicted value", labelsv = "Stability variance", labelby = "Bayesian yield stability index", margin = c(1, 0.8, 0, 0.8), ...)
## S3 method for class 'RGE' plot( x, labelg = "Predicted value", labelsv = "Stability variance", labelby = "Bayesian yield stability index", margin = c(1, 0.8, 0, 0.8), ...)
x |
an object obtained by the function RGE |
labelg |
Label to use in the plot of predicted value of genotype |
labelsv |
Label to use in the plot of stability variance |
labelby |
Label to use in the plot of bayesian yield stability index |
margin |
A numerical vector of the form c(bottom, left, top, right) which gives the margin size specified in inches. |
... |
Further arguments to be passed |
Plot of the predicted values, stability variance and bayesian yield stability index
Jose Miguel Cotes Torres [email protected]
plot
,plot.mcmc
,par
data(m1) plot(m1)
data(m1) plot(m1)
Obtaining the bayes estimative and the highest posterior density intervals at 95% from predicted value of the genotypes, his stability variances and his bayesian yiled stability indexes.
## S3 method for class 'RGE' print(x, ...)
## S3 method for class 'RGE' print(x, ...)
x |
An object obtained by the function RGE |
... |
Further arguments to be passed |
Do not return any value. It is a print version of summary.RGE
coda package is needed.
Jose Miguel Cotes Torres [email protected]
coda
data(m1) print(m1)
data(m1) print(m1)
This function performance the GIBBS sampler for analyze reginals trials.
RGEgibbs(data, gen_c, env_c, blk_c, y_c, prior.g = NULL, prior.vg =NULL,prior.b = NULL, prior.dfb = NULL, prior.sv = NULL, prior.dfsv = NULL, prior.se = NULL, prior.dfse = NULL, burnin = 10, thin = 5, niter = 50, saveAt = 10)
RGEgibbs(data, gen_c, env_c, blk_c, y_c, prior.g = NULL, prior.vg =NULL,prior.b = NULL, prior.dfb = NULL, prior.sv = NULL, prior.dfsv = NULL, prior.se = NULL, prior.dfse = NULL, burnin = 10, thin = 5, niter = 50, saveAt = 10)
data |
|
gen_c |
Number of the column from de data.frame with the genotypes information. |
env_c |
Number of the column from de data.frame with the environment information. |
blk_c |
Number of the column from de data.frame with the block information. |
y_c |
Number of the column from de data.frame with the phenotype information. |
prior.g |
Vector with prior information of the means of genotypes |
prior.vg |
Vector with prior information of the variances of the means of genotypes |
prior.b |
Vector with prior information of the variances of block within environment |
prior.dfb |
Vector with prior information of hyperparameter degree of credibility of the variances of block within environment. |
prior.sv |
Vector with prior information of the stability's variances |
prior.dfsv |
Vector with prior information of hyperparameter degree of credibility of the stability's variances. |
prior.se |
Vector with prior information of the error's variances |
prior.dfse |
Vector with prior information of hyperparameter degree of credibility of the variances of error. |
burnin |
Number of iteration to be consider as burn-in period. This period is not saved in the final result. |
thin |
The thinning interval between consecutive observations. This interval is not saved in the final result. |
niter |
Numbers of iterations to be saved |
saveAt |
Save object outtS4 with samples of the posterior distribution on the work directory each "saveAt" iteration |
Matrix with samples of the posterior distribution
Jose Miguel Cotes Torres [email protected]
Cotes Torres, J. M., Gonzalez Jaimes, E. P., & Cotes Torres, A. (2016). Seleccion de Genotipos con Alta Respuesta y Estabilidad Fenotipica en Pruebas Regionales: Recuperando el Concepto Biologico. Revista Facultad De Ciencias Basicas, 8(2), 226-243.
##data(datos) ##m<-RGEgibbs(data=datos,gen_c=5,env_c=1,blk_c=4,y_c=9, ##thin=5,burnin=100,niter=10000,saveAt=1000)
##data(datos) ##m<-RGEgibbs(data=datos,gen_c=5,env_c=1,blk_c=4,y_c=9, ##thin=5,burnin=100,niter=10000,saveAt=1000)
Obtaining the bayes estimative and the highest posterior density intervals at 95% from predicted value of the genotypes, his stability variances and his bayesian yiled stability indexes.
## S3 method for class 'RGE' summary(object, ...)
## S3 method for class 'RGE' summary(object, ...)
object |
An object obtained by the function RGE |
... |
Further arguments to be passed |
Return a list with:
mu |
Summary with predicted values of genotypes. |
sv |
Summary of the stability variances |
sv |
Summary of the bayesian yield stability indexes |
coda package is needed.
Jose Miguel Cotes Torres [email protected]
coda
data(m1) summary(m1)
data(m1) summary(m1)