Plot method for table of summary of cross validated plsR models

This function provides a table method for the class "summary.cv.plsRmodel"

Usage

# S3 method for class 'table.summary.cv.plsRmodel'
plot(x, type = c("CVMC", "CVQ2", "CVPress"), ...)

Arguments

x

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

type

the type of cross validation criterion to plot.

...

further arguments to be passed to or from methods.

Value

NULL

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. https://www.numdam.org/item/JSFS_2010__151_2_1_0/

See also

summary

Author

Frédéric Bertrand
frederic.bertrand@lecnam.net
https://fbertran.github.io/homepage/

Examples

data(Cornell)
bbb <- cv.plsR(Y~.,data=Cornell,nt=6,K=6,NK=5, verbose=FALSE)
plot(cvtable(summary(bbb)),type="CVQ2")
#> ____************************************************____
#> ____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 

rm(list=c("bbb"))

# \donttest{
data(Cornell)
plot(cvtable(summary(cv.plsR(Y~.,data=Cornell,nt=6,K=6,NK=100, verbose=FALSE))),type="CVQ2")
#> ____************************************************____
#> ____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 89 11 
#> 
#> CV Press criterion:
#>  1  2  3  4  5 
#>  0  0 33 55 12 

# }