vcov.plsdof {plsdof}

Variance-covariance matrix

Description

This function returns the variance-covariance matrix of a plsdof-object.

Usage

## S3 method for class 'plsdof'
vcov(object, ...)

Arguments

object

an object of class "plsdof" that is returned by the function linear.pls

...

additional parameters

Details

The function returns the variance-covariance matrix for the optimal number of components. It can be applied to objects returned by pls.ic and pls.cv.

Value

variance-covariance matrix

Author(s)

Nicole Kraemer

References

Kraemer, N., Sugiyama M. (2011). "The Degrees of Freedom of Partial Least Squares Regression". Journal of the American Statistical Association 106 (494) https://www.tandfonline.com/doi/abs/10.1198/jasa.2011.tm10107

Kraemer, N., Sugiyama M., Braun, M.L. (2009) "Lanczos Approximations for the Speedup of Kernel Partial Least Squares Regression." Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS), p. 272-279

See Also

coef.plsdof, pls.ic, pls.cv

Examples

n<-50 # number of observations
p<-5 # number of variables
X<-matrix(rnorm(n*p),ncol=p)
y<-rnorm(n)


pls.object<-pls.ic(X,y,m=5,criterion="bic")
my.vcov<-vcov(pls.object)
my.sd<-sqrt(diag(my.vcov)) # standard deviation of regression coefficients