plsdof (version 0.3-2)

pls.ic: Model selection for Partial Least Squares based on information criteria

Description

This function computes the optimal model parameters using one of three different model selection criteria (aic, bic, gmdl) and based on two different Degrees of Freedom estimates for PLS.

Usage

pls.ic(
  X,
  y,
  m = min(ncol(X), nrow(X) - 1),
  criterion = "bic",
  naive = FALSE,
  use.kernel = FALSE,
  compute.jacobian = FALSE,
  verbose = TRUE
)

Value

The function returns an object of class "plsdof".

DoF

Degrees of Freedom

m.opt

optimal number of components

sigmahat

vector of estimated model errors

intercept

intercept

coefficients

vector of regression coefficients

covariance

if compute.jacobian=TRUE and use.kernel=FALSE, the function returns the covariance matrix of the optimal regression coefficients.

m.crash

the number of components for which the algorithm returns negative Degrees of Freedom

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.

m

maximal number of Partial Least Squares components. Default is m=ncol(X).

criterion

Choice of the model selection criterion. One of the three options aic, bic, gmdl.

naive

Use the naive estimate for the Degrees of Freedom? Default is FALSE.

use.kernel

Use kernel representation? Default is use.kernel=FALSE.

compute.jacobian

Should the first derivative of the regression coefficients be computed as well? Default is FALSE

verbose

If TRUE, the function prints a warning if the algorithms produce negative Degrees of Freedom. Default is TRUE.

Author

Nicole Kraemer, Mikio L. Braun

Details

There are two options to estimate the Degrees of Freedom of PLS: naive=TRUE defines the Degrees of Freedom as the number of components +1, and naive=FALSE uses the generalized notion of Degrees of Freedom. If compute.jacobian=TRUE, the function uses the Lanczos decomposition to derive the Degrees of Freedom, otherwise, it uses the Krylov representation. (See Kraemer and Sugiyama (2011) for details.) The latter two methods only differ with respect to the estimation of the noise level.

References

Akaikie, H. (1973) "Information Theory and an Extension of the Maximum Likelihood Principle". Second International Symposium on Information Theory, 267 - 281.

Hansen, M., Yu, B. (2001). "Model Selection and Minimum Descripion Length Principle". Journal of the American Statistical Association, 96, 746 - 774

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

Schwartz, G. (1979) "Estimating the Dimension of a Model" Annals of Statistics 26(5), 1651 - 1686.

See Also

pls.model, pls.cv

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.ic(X,y,m=ncol(X))