spls: ci.spls – R documentation

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ci.spls

Calculate bootstrapped confidence intervals of SPLS coefficients

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.

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'

spls

Sparse Partial Least Squares (SPLS) Regression and Classification

v2.2-3

GPL (>= 2)

Authors

Dongjun Chung <chungdon@stat.wisc.edu>, Hyonho Chun <chun@stat.wisc.edu>, Sunduz Keles <keles@stat.wisc.edu>

Initial release

2019-05-04