coefficientPlots: Visualize bootstrapped sampling distribution for linear...

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

Description

Visualize the bootstrapped sampling distribution for linear regression coefficients (Description)

Usage

coefficientPlots(lmodObs, bootList)

Arguments

lmodObs	The observed linear model estimated by least squares. A fitted model object of class inheriting from 'lm'.
bootList	A list containing the bootstrapped sampling distributions of linear regression coefficients. Each element in the list should contain a data frame obtained using one of the six supported bootstrapping methods of size B x (k+1) where k is the number of predictors in the model. Column i of the data frame will be a sample of size B from the sampling distribution of β_{i}.

Details

This function plots the bootstrapped sampling distributions of linear regression coefficients obtained using six different 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. Each plot contains (k+1) facets where k is the number of predictors in the model. The least squares estimate of each regression coefficient is denoted with a red dashed line.

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)
coefficientPlots(lmodObs=lmod, bootList=bootsamps)

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