ci.spls {spls}R Documentation

Calculate the bootstrapped confidence intervals of SPLS coefficients

Description

Calculate the bootstrapped confidence intervals of the coefficients of the selected predictors and draw the confidence interval plots of coefficients.

Usage

ci.spls( object, coverage=0.95, B=1000,
        plot.it=FALSE, plot.fix="y",
        plot.var=NA, K=object$K, fit=object$fit )

Arguments

object A fitted SPLS object.
coverage Coverage of the confidence intervals. coverage should have a number between 0 and 1. Default is 0.95 (95% confidence interval).
B Number of bootstrap iterations. Default is 1000.
plot.it Plot the confidence intervals of the coefficients?
plot.fix If plot.fix="y", then it plots the confidence intervals of the predictors for a given response. If plot.fix="x", then it plots the confidence intervals of a given predictor across all the responses. Relevant only when plot.it=TRUE.
plot.var Index vector of the responses (if plot.fix="y") or predictors (if plot.fix="x") to be fixed in plot.fix. The indices of predictors are defined among the set of the selected predictors. Relevant only when plot.it=TRUE.
K Number of hidden components. Default is to use the same K as in the original SPLS fit.
fit PLS algorithm for model fitting. Alternatives are "kernelpls", "widekernelpls", "simpls", or "oscorespls". Default is to use the same PLS algorithm as in the original SPLS fit.

Value

Invisibly returns a list with components:

cibeta A list with as many matrix elements as the number of responses. Each matrix element is p by 2, where ith row of the matrix lists the upper and lower bounds of the bootstrapped confidence interval of the ith predictor.
betahat Matrix of the original coefficients of the SPLS fit.
lbmat Matrix of the lower bounds of confidence intervals (for internal use).
ubmat Matrix of the upper bounds of confidence intervals (for internal use).

Author(s)

Dongjun Chung, Hyonho Chun, and Sunduz Keles.

References

Chun, H. and Keles, S. (2007). "Sparse partial least squares for simultaneous dimension reduction and variable selection", (http://www.stat.wisc.edu/~keles/Papers/SPLS_Nov07.pdf).

See Also

correct.spls and spls.

Examples

data(mice)
# SPLS with eta=0.6 & 1 hidden components
f <- spls( mice$x, mice$y, K=1, eta=0.6 )
# Calculate the confidence intervals of coefficients
ci.f <- ci.spls( f, plot.it=TRUE, plot.fix="x", plot.var=20 )
# Bootstrapped confidence intervals
cis <- ci.f$cibeta
cis[[20]]   # equivalent, 'cis$1422478_a_at'

[Package spls version 1.0-3 Index]