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

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

## Arguments

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

## Value

variance-covariance matrix

## 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.

## 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

coef.plsdof, pls.ic, pls.cv

Nicole Kraemer

## 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)
#> WARNING: Covariance of regression coefficients is not available.
#> Returning NULL object.my.sd<-sqrt(diag(my.vcov)) # standard deviation of regression coefficients