Correlation matrix for simulating plsR datasets

A correlation matrix to simulate datasets

Format

A data frame with 17 observations on the following 17 variables.

y

a numeric vector

x11

a numeric vector

x12

a numeric vector

x13

a numeric vector

x21

a numeric vector

x22

a numeric vector

x31

a numeric vector

x32

a numeric vector

x33

a numeric vector

x34

a numeric vector

x41

a numeric vector

x42

a numeric vector

x51

a numeric vector

x61

a numeric vector

x62

a numeric vector

x63

a numeric vector

x64

a numeric vector

Source

Handmade.

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

Examples

data(CorMat)
str(CorMat)
#> 'data.frame':	17 obs. of  17 variables:
#>  $ y  : num  1 0.9 0.88 0.92 0.77 0.8 0.65 0.7 0.66 0.6 ...
#>  $ x11: num  0.9 1 0.75 0.8 0.1 0.1 0.05 0.1 0.1 0.05 ...
#>  $ x12: num  0.88 0.75 1 0.65 0.1 0.05 0.1 0.1 0.05 0.1 ...
#>  $ x13: num  0.92 0.8 0.65 1 0.05 0.1 0.15 0.05 0.1 0.15 ...
#>  $ x21: num  0.77 0.1 0.1 0.05 1 0.95 0.1 0.05 0.1 0.1 ...
#>  $ x22: num  0.8 0.1 0.05 0.1 0.95 1 0.05 0.1 0.15 0.05 ...
#>  $ x31: num  0.65 0.05 0.1 0.15 0.1 0.05 1 0.75 0.8 0.92 ...
#>  $ x32: num  0.7 0.1 0.1 0.05 0.05 0.1 0.75 1 0.65 0.55 ...
#>  $ x33: num  0.66 0.1 0.05 0.1 0.1 0.15 0.8 0.65 1 0.7 ...
#>  $ x34: num  0.6 0.05 0.1 0.15 0.1 0.05 0.92 0.55 0.7 1 ...
#>  $ x41: num  0.2 0.1 0.1 0.05 0.1 0.1 0.1 0.1 0.05 0.1 ...
#>  $ x42: num  0.15 0.1 0.05 0.1 0.1 0.05 0.1 0.05 0.1 0.1 ...
#>  $ x51: num  0.1 0.05 0.1 0.15 0.05 0.1 0.05 0.1 0.1 0.1 ...
#>  $ x61: num  0.05 0.1 0.1 0.05 0.1 0.1 0.1 0.1 0.1 0.1 ...
#>  $ x62: num  0.07 0.1 0.05 0.1 0.1 0.05 0.1 0.05 0.05 0.1 ...
#>  $ x63: num  0.08 0.05 0.1 0.15 0.05 0.1 0.05 0.1 0.1 0.05 ...
#>  $ x64: num  0.02 0.15 0.1 0.05 0.1 0.1 0.1 0.1 0.05 0.1 ...