Table method for summary of cross validated PLSR and PLSGLR models

Source: R/cvtable.R

The function cvtable is wrapper of cvtable.plsR and cvtable.plsRglm that provides a table summary for the classes "summary.cv.plsRmodel" and "summary.cv.plsRglmmodel"

cvtable(x, verbose = TRUE, ...)

Arguments

x

an object of the class "summary.cv.plsRmodel"
verbose

should results be displayed ?
...

further arguments to be passed to or from methods.

Value

listList of Information Criteria computed for each fold.

References

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

See also

summary

Author

Frédéric Bertrand
frederic.bertrand@utt.fr
https://fbertran.github.io/homepage/

Examples


data(Cornell)
cv.modpls <- cv.plsR(Y~.,data=Cornell,nt=6,K=6,NK=5)

#> NK: 1 
#> Number of groups : 6 
#> 1 
#> ____************************************************____
#> ____Predicting X without NA neither in X nor in Y____
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Component____ 4 ____
#> ____Component____ 5 ____
#> ____Component____ 6 ____
#> ****________________________________________________****
#> 
#> 2 
#> ____************************************************____
#> ____Predicting X without NA neither in X nor in Y____
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Component____ 4 ____
#> ____Component____ 5 ____
#> ____Component____ 6 ____
#> ****________________________________________________****
#> 
#> 3 
#> ____************************************************____
#> ____Predicting X without NA neither in X nor in Y____
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Component____ 4 ____
#> ____Component____ 5 ____
#> ____Component____ 6 ____
#> ****________________________________________________****
#> 
#> 4 
#> ____************************************************____
#> ____Predicting X without NA neither in X nor in Y____
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Component____ 4 ____
#> ____Component____ 5 ____
#> ____Component____ 6 ____
#> ****________________________________________________****
#> 
#> 5 
#> ____************************************************____
#> ____Predicting X without NA neither in X nor in Y____
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Component____ 4 ____
#> ____Component____ 5 ____
#> Warning : 1 2 3 4 5 6 7 < 10^{-12}
#> Warning only 5 components could thus be extracted
#> ****________________________________________________****
#> 
#> 6 
#> ____************************************************____
#> ____Predicting X without NA neither in X nor in Y____
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Component____ 4 ____
#> ____Component____ 5 ____
#> ____Component____ 6 ____
#> ****________________________________________________****
#> 
#> NK: 2 
#> Number of groups : 6 
#> 1 
#> ____************************************************____
#> ____Predicting X without NA neither in X nor in Y____
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Component____ 4 ____
#> ____Component____ 5 ____
#> ____Component____ 6 ____
#> ****________________________________________________****
#> 
#> 2 
#> ____************************************************____
#> ____Predicting X without NA neither in X nor in Y____
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Component____ 4 ____
#> ____Component____ 5 ____
#> ____Component____ 6 ____
#> ****________________________________________________****
#> 
#> 3 
#> ____************************************************____
#> ____Predicting X without NA neither in X nor in Y____
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Component____ 4 ____
#> ____Component____ 5 ____
#> ____Component____ 6 ____
#> ****________________________________________________****
#> 
#> 4 
#> ____************************************************____
#> ____Predicting X without NA neither in X nor in Y____
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Component____ 4 ____
#> ____Component____ 5 ____
#> Warning : 1 2 3 4 5 6 7 < 10^{-12}
#> Warning only 5 components could thus be extracted
#> ****________________________________________________****
#> 
#> 5 
#> ____************************************************____
#> ____Predicting X without NA neither in X nor in Y____
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Component____ 4 ____
#> ____Component____ 5 ____
#> ____Component____ 6 ____
#> ****________________________________________________****
#> 
#> 6 
#> ____************************************************____
#> ____Predicting X without NA neither in X nor in Y____
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Component____ 4 ____
#> ____Component____ 5 ____
#> ____Component____ 6 ____
#> ****________________________________________________****
#> 
#> NK: 3 
#> Number of groups : 6 
#> 1 
#> ____************************************************____
#> ____Predicting X without NA neither in X nor in Y____
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Component____ 4 ____
#> ____Component____ 5 ____
#> ____Component____ 6 ____
#> ****________________________________________________****
#> 
#> 2 
#> ____************************************************____
#> ____Predicting X without NA neither in X nor in Y____
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Component____ 4 ____
#> ____Component____ 5 ____
#> Warning : 1 2 3 4 5 6 7 < 10^{-12}
#> Warning only 5 components could thus be extracted
#> ****________________________________________________****
#> 
#> 3 
#> ____************************************************____
#> ____Predicting X without NA neither in X nor in Y____
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Component____ 4 ____
#> ____Component____ 5 ____
#> ____Component____ 6 ____
#> ****________________________________________________****
#> 
#> 4 
#> ____************************************************____
#> ____Predicting X without NA neither in X nor in Y____
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Component____ 4 ____
#> ____Component____ 5 ____
#> ____Component____ 6 ____
#> ****________________________________________________****
#> 
#> 5 
#> ____************************************************____
#> ____Predicting X without NA neither in X nor in Y____
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Component____ 4 ____
#> ____Component____ 5 ____
#> ____Component____ 6 ____
#> ****________________________________________________****
#> 
#> 6 
#> ____************************************************____
#> ____Predicting X without NA neither in X nor in Y____
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Component____ 4 ____
#> ____Component____ 5 ____
#> ____Component____ 6 ____
#> ****________________________________________________****
#> 
#> NK: 4 
#> Number of groups : 6 
#> 1 
#> ____************************************************____
#> ____Predicting X without NA neither in X nor in Y____
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Component____ 4 ____
#> ____Component____ 5 ____
#> Warning : 1 2 3 4 5 6 7 < 10^{-12}
#> Warning only 5 components could thus be extracted
#> ****________________________________________________****
#> 
#> 2 
#> ____************************************************____
#> ____Predicting X without NA neither in X nor in Y____
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Component____ 4 ____
#> ____Component____ 5 ____
#> ____Component____ 6 ____
#> ****________________________________________________****
#> 
#> 3 
#> ____************************************************____
#> ____Predicting X without NA neither in X nor in Y____
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Component____ 4 ____
#> ____Component____ 5 ____
#> ____Component____ 6 ____
#> ****________________________________________________****
#> 
#> 4 
#> ____************************************************____
#> ____Predicting X without NA neither in X nor in Y____
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Component____ 4 ____
#> ____Component____ 5 ____
#> ____Component____ 6 ____
#> ****________________________________________________****
#> 
#> 5 
#> ____************************************************____
#> ____Predicting X without NA neither in X nor in Y____
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Component____ 4 ____
#> ____Component____ 5 ____
#> ____Component____ 6 ____
#> ****________________________________________________****
#> 
#> 6 
#> ____************************************************____
#> ____Predicting X without NA neither in X nor in Y____
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Component____ 4 ____
#> ____Component____ 5 ____
#> ____Component____ 6 ____
#> ****________________________________________________****
#> 
#> NK: 5 
#> Number of groups : 6 
#> 1 
#> ____************************************************____
#> ____Predicting X without NA neither in X nor in Y____
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Component____ 4 ____
#> ____Component____ 5 ____
#> ____Component____ 6 ____
#> ****________________________________________________****
#> 
#> 2 
#> ____************************************************____
#> ____Predicting X without NA neither in X nor in Y____
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Component____ 4 ____
#> ____Component____ 5 ____
#> ____Component____ 6 ____
#> ****________________________________________________****
#> 
#> 3 
#> ____************************************************____
#> ____Predicting X without NA neither in X nor in Y____
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Component____ 4 ____
#> ____Component____ 5 ____
#> ____Component____ 6 ____
#> ****________________________________________________****
#> 
#> 4 
#> ____************************************************____
#> ____Predicting X without NA neither in X nor in Y____
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Component____ 4 ____
#> ____Component____ 5 ____
#> ____Component____ 6 ____
#> ****________________________________________________****
#> 
#> 5 
#> ____************************************************____
#> ____Predicting X without NA neither in X nor in Y____
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Component____ 4 ____
#> ____Component____ 5 ____
#> ____Component____ 6 ____
#> ****________________________________________________****
#> 
#> 6 
#> ____************************************************____
#> ____Predicting X without NA neither in X nor in Y____
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Component____ 4 ____
#> ____Component____ 5 ____
#> Warning : 1 2 3 4 5 6 7 < 10^{-12}
#> Warning only 5 components could thus be extracted
#> ****________________________________________________****
#> 
res.cv.modpls <- cvtable(summary(cv.modpls))

#> ____************************************************____
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Component____ 4 ____
#> ____Component____ 5 ____
#> ____Component____ 6 ____
#> ____Predicting X without NA neither in X nor in Y____
#> ****________________________________________________****
#> 
#> 
#> NK: 1,  2,  3,  4,  5
#> 
#> CV Q2 criterion:
#> 0 1 
#> 0 5 
#> 
#> CV Press criterion:
#> 1 2 3 4 
#> 0 0 4 1 
plot(res.cv.modpls) #defaults to type="CVQ2"

rm(list=c("cv.modpls","res.cv.modpls"))

# \donttest{
data(Cornell)
cv.modpls <- cv.plsR(Y~.,data=Cornell,nt=6,K=6,NK=25,verbose=FALSE)
res.cv.modpls <- cvtable(summary(cv.modpls))

#> ____************************************************____
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Component____ 4 ____
#> ____Component____ 5 ____
#> ____Component____ 6 ____
#> ____Predicting X without NA neither in X nor in Y____
#> ****________________________________________________****
#> 
#> 
#> NK: 1,  2,  3,  4,  5,  6,  7,  8,  9,  10
#> NK: 11,  12,  13,  14,  15,  16,  17,  18,  19,  20
#> NK: 21,  22,  23,  24,  25
#> 
#> CV Q2 criterion:
#>  0  1  2 
#>  0 23  2 
#> 
#> CV Press criterion:
#>  1  2  3  4  5 
#>  0  0  7 14  4 
plot(res.cv.modpls) #defaults to type="CVQ2"

rm(list=c("cv.modpls","res.cv.modpls"))
  
data(Cornell)
cv.modpls <- cv.plsR(Y~.,data=Cornell,nt=6,K=6,NK=100,verbose=FALSE)
res.cv.modpls <- cvtable(summary(cv.modpls))

#> ____************************************************____
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Component____ 4 ____
#> ____Component____ 5 ____
#> ____Component____ 6 ____
#> ____Predicting X without NA neither in X nor in Y____
#> ****________________________________________________****
#> 
#> 
#> NK: 1,  2,  3,  4,  5,  6,  7,  8,  9,  10
#> NK: 11,  12,  13,  14,  15,  16,  17,  18,  19,  20
#> NK: 21,  22,  23,  24,  25,  26,  27,  28,  29,  30
#> NK: 31,  32,  33,  34,  35,  36,  37,  38,  39,  40
#> NK: 41,  42,  43,  44,  45,  46,  47,  48,  49,  50
#> NK: 51,  52,  53,  54,  55,  56,  57,  58,  59,  60
#> NK: 61,  62,  63,  64,  65,  66,  67,  68,  69,  70
#> NK: 71,  72,  73,  74,  75,  76,  77,  78,  79,  80
#> NK: 81,  82,  83,  84,  85,  86,  87,  88,  89,  90
#> NK: 91,  92,  93,  94,  95,  96,  97,  98,  99,  100
#> 
#> CV Q2 criterion:
#>  0  1  2 
#>  0 87 13 
#> 
#> CV Press criterion:
#>  1  2  3  4  5 
#>  0  0 40 52  8 
plot(res.cv.modpls) #defaults to type="CVQ2"

rm(list=c("cv.modpls","res.cv.modpls"))

data(Cornell)
cv.modplsglm <- cv.plsRglm(Y~.,data=Cornell,nt=6,K=6,
modele="pls-glm-gaussian",NK=100,verbose=FALSE)
res.cv.modplsglm <- cvtable(summary(cv.modplsglm))

#> ____************************************************____
#> 
#> Family: gaussian 
#> Link function: identity 
#> 
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Component____ 4 ____
#> ____Component____ 5 ____
#> ____Component____ 6 ____
#> ____Predicting X without NA neither in X or Y____
#> ****________________________________________________****
#> 
#> 
#> NK: 1,  2,  3,  4,  5,  6,  7,  8,  9,  10
#> NK: 11,  12,  13,  14,  15,  16,  17,  18,  19,  20
#> NK: 21,  22,  23,  24,  25,  26,  27,  28,  29,  30
#> NK: 31,  32,  33,  34,  35,  36,  37,  38,  39,  40
#> NK: 41,  42,  43,  44,  45,  46,  47,  48,  49,  50
#> NK: 51,  52,  53,  54,  55,  56,  57,  58,  59,  60
#> NK: 61,  62,  63,  64,  65,  66,  67,  68,  69,  70
#> NK: 71,  72,  73,  74,  75,  76,  77,  78,  79,  80
#> NK: 81,  82,  83,  84,  85,  86,  87,  88,  89,  90
#> NK: 91,  92,  93,  94,  95,  96,  97,  98,  99,  100
#> 
#> CV Q2Chi2 criterion:
#>  0  1  2 
#>  0 26 74 
#> 
#> CV PreChi2 criterion:
#>  1  2  3  4  5 
#>  0 16 64 19  1 
plot(res.cv.modplsglm) #defaults to type="CVQ2Chi2"

rm(list=c("res.cv.modplsglm"))
# }