R: Calculate bootstrapped confidence intervals of SPLS...

ci.spls {spls} | R Documentation

## 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.

### 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'

[Package spls version 2.2-2 Index]