View source: R/plot.table.summary.cv.plsRglmmodel.R
plot.table.summary.cv.plsRglmmodel | R Documentation |
This function provides a table method for the class
"summary.cv.plsRglmmodel"
## S3 method for class '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
Frédéric Bertrand
frederic.bertrand@utt.fr
https://fbertran.github.io/homepage/
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
summary
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")
rm(list=c("bbb"))
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")
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