bootstrapSamples: Obtain bootstrap samples for linear regression coefficients

In ndelrocco/lmBootCompare: Compares Six Methods of Bootstrapping Linear Model Coefficients

Description

Obtain bootstrap samples for linear regression coefficients using six different methods.

Usage

bootstrapSamples(lmodObs, formula, data, B)

Arguments

lmodObs	The observed linear model estimated by least squares. A fitted model object of class inheriting from 'lm'.
formula	A string that can be coerced into class 'formula'. Usually of the form response variable \sim predictor variables. A symbolic description of the model to be fitted.
data	data frame, list or environment (or object coercible by as.data.frame to a data frame) containing the variables in the model.
B	The number of bootstrap replicates. Usually this will be a single positive integer.

Details

This function is a wrapper for bootCases, bootResids, and bootWild. This function obtains the bootstrapped sampling distributions for linear regression coefficients obtained in each of six methods: case resampling, residual resampling, Wild bootstrap with Mammen's Two-Point Distribution, Wild bootstrap with Mammen's Continuous Distribution, Wild boostrap with Rademacher Distribution, and Wild bootstrap with Standard Normal Distribution.

Author(s)

Natalie DelRocco

Examples

library(faraway)
data(prostate)
lmod <- lm(lpsa ~ lcavol + age + lweight, prostate)
bootsamps <- bootstrapSamples(lmodObs=lmod, formula='lpsa ~ lcavol + age + lweight',
                              data=prostate, B=1000)

ndelrocco/lmBootCompare documentation built on Dec. 10, 2019, 12:38 p.m.