bootcoefs: Bootstrap the regression coefficients for a robust linear...

Description Usage Arguments Details Value Methods (by class) References Examples

Description

This function provides an easy interface and useful output to bootstrapping the regression coefficients of robust linear regression models

Usage

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bootcoefs(object, R = 999, method = c("frb", "residuals", "cases"),
  ncpus = NULL, cl = NULL, ...)

## S3 method for class 'complmrob'
bootcoefs(object, R = 999, method = c("frb",
  "residuals", "cases"), ncpus = NULL, cl = NULL, ...)

## S3 method for class 'lmrob'
bootcoefs(object, R = 999, method = c("frb", "residuals",
  "cases"), ncpus = NULL, cl = NULL, ...)

Arguments

object

the model to bootstrap the coefficients from

R

the number of bootstrap replicates.

method

one of "frb" for fast and robust bootstrap, "residuals" to resample the residuals or "cases" to resample the cases.

ncpus

the number of CPUs to utilize for bootstrapping.

cl

a snow or parallel cluster to use for bootstrapping.

...

currently ignored.

Details

The default method is to use fast and robust bootstrap as described in the paper by M. Salibian-Barrera, et al. (see references). The other options are to bootstrap the residuals or to bootstrap cases (observations), but the sampling distribution of the estimates from these methods can be numerically instable and take longer to compute.

Value

A list of type bootcoefs for which print, summary and plot methods are available

Methods (by class)

  • complmrob: For robust linear regression models with compositional data

  • lmrob: For standard robust linear regression models

References

M. Salibian-Barrera, S. Aelst, and G. Willems. Fast and robust bootstrap. Statistical Methods and Applications, 17(1):41-71, 2008.

Examples

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data <- data.frame(lifeExp = state.x77[, "Life Exp"], USArrests[ , -3])
mUSArr <- complmrob(lifeExp ~ ., data = data)
bc <- bootcoefs(mUSArr, R = 200) # the number of bootstrap replicates should
                                 # normally be higher!
summary(bc)
plot(bc) # for the model diagnostic plots


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