pls.cv: Model selection for Partial Least Squares based on...

In plsdof: Degrees of Freedom and Statistical Inference for Partial Least Squares Regression

Model selection for Partial Least Squares based on cross-validation

Description

This function computes the optimal model parameter using cross-validation.

Usage

pls.cv(
  X,
  y,
  k = 10,
  groups = NULL,
  m = ncol(X),
  use.kernel = FALSE,
  compute.covariance = FALSE,
  method.cor = "pearson"
)

Arguments

X	matrix of predictor observations.
y	vector of response observations. The length of y is the same as the number of rows of X.
k	number of cross-validation splits. Default is 10.
groups	an optional vector with the same length as y. It encodes a partitioning of the data into distinct subgroups. If groups is provided, k=10 is ignored and instead, cross-validation is performed based on the partioning. Default is NULL.
m	maximal number of Partial Least Squares components. Default is m=ncol(X).
use.kernel	Use kernel representation? Default is use.kernel=FALSE.
compute.covariance	If TRUE, the function computes the covariance for the cv-optimal regression coefficients.
method.cor	How should the correlation to the response be computed? Default is ”pearson”.

Details

The data are centered and scaled to unit variance prior to the PLS algorithm. It is possible to estimate the covariance matrix of the cv-optimal regression coefficients (compute.covariance=TRUE). Currently, this is only implemented if use.kernel=FALSE.

Value

cv.error.matrix	matrix of cross-validated errors based on mean squared error. A row corresponds to one cross-validation split.
cv.error	vector of cross-validated errors based on mean squared error
m.opt	optimal number of components based on mean squared error
intercept	intercept of the optimal model, based on mean squared error
coefficients	vector of regression coefficients of the optimal model, based on mean squared error
cor.error.matrix	matrix of cross-validated errors based on correlation. A row corresponds to one cross-validation split.
cor.error	vector of cross-validated errors based on correlation
m.opt.cor	optimal number of components based on correlation
intercept.cor	intercept of the optimal model, based on correlation
coefficients.cor	vector of regression coefficients of the optimal model, based on mean squared error
covariance	If TRUE and use.kernel=FALSE, the covariance of the cv-optimal regression coefficients (based on mean squared error) is returned.

Author(s)

Nicole Kraemer, Mikio L. Braun

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., Braun, M.L. (2007) "Kernelizing PLS, Degrees of Freedom, and Efficient Model Selection", Proceedings of the 24th International Conference on Machine Learning, Omni Press, 441 - 448

See Also

pls.model, pls.ic

Examples

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

# compute linear PLS
pls.object<-pls.cv(X,y,m=ncol(X))

# define random partioning
groups<-sample(c("a","b","c"),n,replace=TRUE)
pls.object1<-pls.cv(X,y,groups=groups)

plsdof documentation built on Dec. 1, 2022, 1:13 a.m.