Type: Package
Title: Response from Genotype to Environment
Depends: R (≥ 3.5.0), Matrix, MatrixModels, coda
Version: 1.0
Date: 2021-10-21
Author: Jose Miguel Cotes Torres. Universidad Nacional de Colombia - Sede Medellin
Maintainer: Jose Miguel Cotes Torres <jmcotes@unal.edu.co>
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.
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
Packaged: 2021-10-27 19:44:34 UTC; jose
Repository: CRAN
Date/Publication: 2021-10-28 19:50:02 UTC

Response from Genotype to Environment

Description

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.

Author(s)

Jose Miguel Cotes Torres jmcotes@unal.edu.co

References

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.

Response from Genotype to Environment

Description

This function performance the GIBBS sampler for analyze reginals trials.

Usage

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)

Arguments

data

data.frame

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

Value

Matrix with samples of the posterior distribution

Author(s)

Jose Miguel Cotes Torres jmcotes@unal.edu.co

References

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.

Examples

##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)

Summary of the posterior distribution

Description

Obtaining the bayes estimative and the highest posterior density intervals at 95% from a object obtained by RGE function.

Usage

bayes.posterior(x, ...)

Arguments

x

An object obtained by the function RGE "RGE"

...

Further arguments to be passed

Value

Dataframe with the summary of the posterior distribution

Note

coda package is needed.

Author(s)

Jose Miguel Cotes Torres jmcotes@unal.edu.co

See Also

coda

Examples

data(m1)
bayes.posterior(m1)

Potato regional trial in Colombia

Description

Eleven trials with ten genotypes stablished in state of Narino-Colombia Cortesy: Professor Luis Ernesto Rodriguez Molano lerodriguezmo@unal.edu.co

Usage

data("datos")

Format

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

References

Unpublished data.

Examples

data(datos)

Samples of the posterior distribution by GIBBS sampler

Description

Object obtained by function RGE

Usage

data("m1")

Format

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

Examples

data(m1)

Plots for object obtained by RGE

Description

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.

Usage

## 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), ...)

Arguments

x

an object obtained by the function RGE "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

Value

Plot of the predicted values, stability variance and bayesian yield stability index

Author(s)

Jose Miguel Cotes Torres jmcotes@unal.edu.co

See Also

plot,plot.mcmc,par

Examples

  data(m1)
  plot(m1)

Summary of the posterior distribution

Description

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.

Usage

## S3 method for class 'RGE'
print(x, ...)

Arguments

x

An object obtained by the function RGE "RGE"

...

Further arguments to be passed

Value

Do not return any value. It is a print version of summary.RGE

Note

coda package is needed.

Author(s)

Jose Miguel Cotes Torres jmcotes@unal.edu.co

See Also

coda

Examples

  data(m1)
  print(m1)

Summary of the posterior distribution

Description

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.

Usage

## S3 method for class 'RGE'
summary(object, ...)

Arguments

object

An object obtained by the function RGE "RGE"

...

Further arguments to be passed

Value

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

Note

coda package is needed.

Author(s)

Jose Miguel Cotes Torres jmcotes@unal.edu.co

See Also

coda

Examples

  data(m1)
  summary(m1)

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