Authors: Qian M. Zhou, Jingjing Pan,
Vignette: Joint Modeling in Analyzing Highly Unbalanced Multi-Environment Trial Data (under review)
Please email all comments/questions to qz70@msstate.edu or panjj1125@outlook.com
You can load the package and use the function install_github
library(devtools)
install_github("panjj1125/CVEPJointModel",dependencies=TRUE)
Note that this will install all the packages suggested and required to run our package. It may take a few minutes the first time, but this only needs to be done on the first use. In the future you can update to the most recent development version using the same code.
The main function to estimate the model is CVEP_JM() but there are a host of other useful functions. Here is one demo:
library(CVEPJointModel)
data(NCVT_3year)
Model <- CVEP_JM(NCVT_3year, factors = c("Year","Loc","Rep", "Variety"),
TT_mm=list(fixed=c("Year","Loc"),
random=c("Variety","Variety:Year","Variety:Loc")),
DS_mm=list(fixed=c("Year","Loc"),random=c("Variety")),
converg_control=list(nsamp=500,max.iter=1000,err=10^(-7),err1=10^(-4),seed=20190421))
When there is controls in fixed effects, we can use the following code:
Model <- CVEP_JM(NCVT_3year, factors = c("Year","Loc","Rep", "Variety", "Checks"), TT_mm=list(fixed=c("Year","Loc", "Checks"),random=c("Variety","Variety:Year","Variety:Loc")),DS_mm=list(fixed=c("Year","Loc"),random=c("Variety")),converg_control=list(nsamp=500,max.iter=1000,err=10^(-7),err1=10^(-4),seed=20190421))
To predict the random effects, we use
r <- raneff(Model)
To obtain the results in our paper, you can run
example(NCVT_3year)
Please cite the code using following formula:
@misc{Qian2019,
author = {Qian M. Zhou, Jingjing Pan},
title = {CVEPJointModel: An R Package for Joint Modelling Procedure in Analyzing Highly Unbalanced CVEP Data},
year = {2019},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/panjj1125/CVEPJointModel}},
}