| Type: | Package |
| Title: | Resampling Based Yield Stability Analyses |
| Version: | 1.0.0 |
| Author: | Jixiang Wu |
| Maintainer: | Jixiang Wu <jixiang.wu@sdstate.edu> |
| Description: | Several yield stability analyses are mentioned in this package: variation and regression based yield stability analyses. Resampling techniques are integrated with these stability analyses. The function stab.mean() provides the genotypic means and ranks including their corresponding confidence intervals. The function stab.var() provides the genotypic variances over environments including their corresponding confidence intervals. The function stab.fw() is an extended method from the Finlay-Wilkinson method (1963). This method can include several other factors that might impact yield stability. Resampling technique is integrated into this method. A few missing data points or unbalanced data are allowed too. The function stab.fw.check() is an extended method from the Finlay-Wilkinson method (1963). The yield stability is evaluated via common check line(s). Resampling technique is integrated. |
| License: | GPL-2 |
| Encoding: | UTF-8 |
| LazyData: | true |
| RoxygenNote: | 7.1.1 |
| Depends: | R(≥ 4.0.0) |
| NeedsCompilation: | no |
| Packaged: | 2021-10-22 18:07:26 UTC; Jixiang.Wu |
| Repository: | CRAN |
| Date/Publication: | 2021-10-26 14:30:02 UTC |
Maize yield trial data
Description
Maize yield trial data
Usage
maize
Format
An object of class data.frame with 260 rows and 4 columns.
References
Fan X.M., Kang M.S., Chen H.M., Zhang Y.D., Tan J., Xu C.X. (2007) Yield stability of maize hybrids evaluated in multi-environment trials in Yunnan, China. Agronomy Journal.99:220-228
Examples
str(maize)
F-W Regression Based Yield Stability Analysis
Description
F-W Regression Based Yield Stability Analysis
Usage
stab.fw(y, Gen, Env, times, Rep, X = NULL, alpha = NULL)
Arguments
y |
A vector of yield data |
Gen |
A vector of Genotypes |
Env |
A vector of Environments |
times |
Replication number for resampling |
Rep |
Replication included or not included |
X |
Independent variables matrix or vector |
alpha |
Preset alpha value |
Value
A list of yield stability results
References
Finlay, K.W., G.N. Wilkinson 1963. The analysis of adaptation in a plant breeding programme. Australian Journal of Agricultural Research 14: 742-754.
Wu, J., K. Glover, W. Berzonsky, 2012. Statistical tests for stability analysis with resampling techniques. 25th Conference of Applied Statistics in Agriculture. p88-108. April 29-May 01, 2012. Manhattan, KS
Wu, J., K. Glover, and N. Mueller 2014. Check based stability analysis method and its application to winter wheat variety trials," Conference on Applied Statistics in Agriculture. https://doi.org/10.4148/2475-7772.1006
Examples
require(genstab)
data(maize)
#names(maize)
Geno=as.vector(maize$Cultivar)
Env=paste(maize$Location,maize$Year,sep=":")
y=maize$Yld
res=stab.fw(y,Gen=Geno,Env=Env,times=10,Rep=TRUE)
res
##end
Check-based yield stability analysis
Description
Check-based yield stability analysis
Usage
stab.fw.check(y, Gen, Env, times, check, Rep, X = NULL, alpha = NULL)
Arguments
y |
A response varible vector used for stability analysis |
Gen |
A vector of genotypes. |
Env |
A vector of environments. |
times |
Times of resampling used for stability analysis. |
check |
One or more checks used for stability analysis. |
Rep |
An argument with replication: Rep=TRUE or with replication: Rep=FALSE |
X |
A vector or matrix of other predictable variables. Default is NULL. |
alpha |
A nominal probability values used for statistical tests. Default is NULL, 0.05 |
Value
A list of yield stability results
References
Finlay, K.W., G.N. Wilkinson 1963. The analysis of adaptation in a plant breeding programme. Australian Journal of Agricultural Research 14: 742-754.
Wu, J., K. Glover, W. Berzonsky, 2012. Statistical tests for stability analysis with resampling techniques. 25th Conference of Applied Statistics in Agriculture. p88-108. April 29-May 01, 2012. Manhattan, KS
Wu, J., K. Glover, and N. Mueller 2014. Check based stability analysis method and its application to winter wheat variety trials," Conference on Applied Statistics in Agriculture. P102-114. https://doi.org/10.4148/2475-7772.1006
Examples
data(maize)
#names(maize)
Geno=as.vector(maize$Cultivar)
Env=paste(maize$Location,maize$Year,sep=":")
y=maize$Yld
res=stab.fw.check(y,Gen=Geno,Env=Env,times=10,check=c("Hai He"),Rep=FALSE)
res
Group means and ranks with resampling
Description
Group mean and rank calculation with two resampling techniques:permuation and bootstraping
Usage
stab.mean(Y, class, cls2 = NULL, resample, times = NULL, alpha = NULL)
Arguments
Y |
A matrix including One or more traits |
class |
A vector of the first factor for calculating variance. For example, a vector of genotypes. |
cls2 |
A vector of the second factor used within-group bootstraping for variance. It can be default |
resample |
Resampling technique option. resample="Boot" is for bootstrapping. resample="Perm" is for permutation. |
times |
Number of resampling used. The default number is 1000. |
alpha |
A nomimal probability used for statistical test. The default value is 0.05. |
Value
A list of variances and confidence intervals for genotypes or environments
Author(s)
Jixiang Wu <jixiang.wu@sdstate.edu>
References
Finlay, K.W., G.N. Wilkinson 1963. The analysis of adaptation in a plant breeding programme. Australian Journal of Agricultural Research 14: 742-754.
Wu, J., K. Glover, W. Berzonsky, 2012. Statistical tests for stability analysis with resampling techniques. 25th Conference of Applied Statistics in Agriculture. p88-108. April 29- May 01, 2012. Manhattan, KS
Examples
data(maize)
#names(maize)
Geno=as.vector(maize$Cultivar)
Env=paste(maize$Location,maize$Year,sep=":")
y=maize$Yld
res=stab.mean(y,class=Geno,cls2=Env,resample="Boot",times=100)
res
res=stab.mean(y,class=Geno,resample="Perm",times=100)
res
Group variances with resampling
Description
Group variance calculation with two resampling techniques:permuation and bootstraping
Usage
stab.var(Y, class, cls2 = NULL, resample, times = NULL, alpha = NULL)
Arguments
Y |
A matrix including One or more traits |
class |
A vector of the first factor for calculating variance. For example, a vector of genotypes. |
cls2 |
A vector of the second factor used within-group bootstraping for variance. It can be default |
resample |
Resampling technique option. resample="Boot" is for bootstrapping. resample="Perm" is for permutation. |
times |
Number of resampling used. The default number is 1000. |
alpha |
A nomimal probability used for statistical test. The default value is 0.05. |
Value
A list of variances and confidence intervals for genotypes or environments
Author(s)
Jixiang Wu <jixiang.wu@sdstate.edu>
References
Finlay, K.W., G.N. Wilkinson 1963. The analysis of adaptation in a plant breeding programme. Australian Journal of Agricultural Research 14: 742-754.
Wu, J., K. Glover, W. Berzonsky, 2012. Statistical tests for stability analysis with resampling techniques. 25th Conference of Applied Statistics in Agriculture. p88-108. April 29- May 01, 2012. Manhattan, KS
Examples
data(maize)
#names(maize)
Geno=as.vector(maize$Cultivar)
Env=paste(maize$Location,maize$Year,sep=":")
y=maize$Yld
res=stab.var(y,class=Geno,cls2=Env,resample="Boot",times=100)
res
res=stab.var(y,class=Geno,resample="Perm",times=100)
res