R/plot.table.summary.cv.plsRglmmodel.R
plot.table.summary.cv.plsRglmmodel.Rd
This function provides a table method for the class
"summary.cv.plsRglmmodel"
# S3 method for table.summary.cv.plsRglmmodel plot(x, type = c("CVMC", "CVQ2Chi2", "CVPreChi2"), ...)
x | an object of the class |
---|---|
type | the type of cross validation criterion to plot. |
... | further arguments to be passed to or from methods. |
NULL
Nicolas Meyer, Myriam Maumy-Bertrand et Frédéric Bertrand (2010). Comparing the linear and the logistic PLS regression with qualitative predictors: application to allelotyping data. Journal de la Societe Francaise de Statistique, 151(2), pages 1-18. http://publications-sfds.math.cnrs.fr/index.php/J-SFdS/article/view/47
Frédéric Bertrand
frederic.bertrand@utt.fr
https://fbertran.github.io/homepage/
data(Cornell) bbb <- cv.plsRglm(Y~.,data=Cornell,nt=10,NK=1, modele="pls-glm-family",family=gaussian(), verbose=FALSE) plot(cvtable(summary(bbb,verbose=FALSE)),type="CVQ2Chi2")#> #> CV Q2Chi2 criterion: #> 0 1 #> 0 1 #> #> CV PreChi2 criterion: #> 1 2 3 #> 0 0 1rm(list=c("bbb")) # \donttest{ data(Cornell) plot(cvtable(summary(cv.plsRglm(Y~.,data=Cornell,nt=10,NK=100, modele="pls-glm-family",family=gaussian(), verbose=FALSE), verbose=FALSE)),type="CVQ2Chi2")#> #> CV Q2Chi2 criterion: #> 0 1 2 #> 0 30 70 #> #> CV PreChi2 criterion: #> 1 2 3 4 5 #> 0 22 54 21 3# }