ci.spls: Calculate bootstrapped confidence intervals of SPLS...

In spls: Sparse Partial Least Squares (SPLS) Regression and Classification

Description

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

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 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 confidence intervals of coefficients?
plot.fix	If plot.fix="y", then plot confidence intervals of the predictors for a given response. If plot.fix="x", then plot confidence intervals of a given predictor across all the responses. Relevant only when plot.it=TRUE.
plot.var	Index vector of 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 i-th row of the matrix lists the upper and lower bounds of the bootstrapped confidence interval of the i-th predictor.
betahat	Matrix of original coefficients of the SPLS fit.
lbmat	Matrix of lower bounds of confidence intervals (for internal use).
ubmat	Matrix of upper bounds of confidence intervals (for internal use).

Author(s)

Dongjun Chung, Hyonho Chun, and Sunduz Keles.

References

Chun H and Keles S (2010), "Sparse partial least squares for simultaneous dimension reduction and variable selection", Journal of the Royal Statistical Society - Series B, Vol. 72, pp. 3–25.

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 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'

Example output

Sparse Partial Least Squares (SPLS) Regression and
Classification (version 2.2-3)

10 % completed...
20 % completed...
30 % completed...
40 % completed...
50 % completed...
60 % completed...
70 % completed...
80 % completed...
90 % completed...
100 % completed...
                  2.5%       97.5%
D2Mit274  -0.003970052 0.023560092
D2Mit17   -0.003963566 0.023294933
D2Mit106  -0.004414759 0.023867315
D2Mit194  -0.004841382 0.021994504
D2Mit263  -0.004971123 0.023814272
D2Mit51   -0.005093959 0.023262455
D2Mit49   -0.005229769 0.021756809
D2Mit229  -0.003862827 0.022523213
D2Mit148  -0.002497678 0.021722692
D5Mit348  -0.003038694 0.020173998
D5Mit75   -0.003534721 0.023448835
D5Mit267  -0.003816926 0.022685136
D5Mit259  -0.004529217 0.023886364
D5Mit9    -0.004961164 0.026057401
D5Mit240  -0.004736207 0.027361691
D5Mit136  -0.005360719 0.027098222
D8Mit249  -0.018270663 0.003446075
D8Mit211  -0.021114005 0.003050463
D8Mit113  -0.020499521 0.003261824
D9Mit206  -0.019773859 0.002414847
D9Mit2    -0.020033353 0.002504833
D9Mit21   -0.017421480 0.002141814
D9Mit207  -0.017201864 0.002529220
D9Mit8    -0.016983005 0.002392261
D9Mit15   -0.023668777 0.001924868
D9Mit18   -0.021697655 0.001661768
D15Mit174 -0.022867499 0.001920104
D15Mit136 -0.026046222 0.002208022
D15Mit63  -0.026483401 0.001640367
D15Mit107 -0.022090748 0.003185885

spls documentation built on May 6, 2019, 1:09 a.m.