clusterSEs (version 2.2)

cluster.bs.glm: Pairs Cluster Bootstrapped p-Values For GLM

Description

This software estimates p-values using pairs cluster bootstrapped t-statistics for GLM models (Cameron, Gelbach, and Miller 2008). The data set is repeatedly re-sampled by cluster, a model is estimated, and inference is based on the sampling distribution of the pivotal (t) statistic.

Usage

cluster.bs.glm(mod, dat, cluster, ci.level = 0.95, boot.reps = 1000,
  stratify = FALSE, cluster.se = TRUE, report = TRUE, prog.bar = TRUE)

Arguments

mod

A model estimated using glm.

dat

The data set used to estimate mod.

cluster

A formula of the clustering variable.

ci.level

What confidence level should CIs reflect?

boot.reps

The number of bootstrap samples to draw.

stratify

Sample clusters only (= FALSE) or clusters and observations by cluster (= TRUE).

cluster.se

Use clustered standard errors (= TRUE) or ordinary SEs (= FALSE) for bootstrap replicates.

report

Should a table of results be printed to the console?

prog.bar

Show a progress bar of the bootstrap (= TRUE) or not (= FALSE).

Value

• A list with the elements
• p.values
  A matrix of the estimated p-values.
• ci
  A matrix of confidence intervals.

References

Cameron, A. Colin, Jonah B. Gelbach, and Douglas L. Miller. 2008. "Bootstrap-Based Improvements for Inference with Clustered Errors." The Review of Economics and Statistics 90(3): 414-427.

Examples

# example one: predict whether respondent has a university degree
require(effects)
data(WVS)
logit.model <- glm(degree ~ religion + gender + age, data=WVS, family=binomial(link="logit"))
summary(logit.model)

# compute pairs cluster bootstrapped p-values
clust.bs.p <- cluster.bs.glm(logit.model, WVS, ~ country, report = T)



# example two: predict chicken weight
rm(list=ls())
data(ChickWeight)

dum <- model.matrix(~ ChickWeight$Diet)
ChickWeight$Diet2 <- as.numeric(dum[,2])
ChickWeight$Diet3 <- as.numeric(dum[,3])
ChickWeight$Diet4 <- as.numeric(dum[,4])

weight.mod2 <- glm(formula = weight~Diet2+Diet3+Diet4+log(Time+1),data=ChickWeight)

# compute pairs cluster bootstrapped p-values
clust.bs.w <- cluster.bs.glm(weight.mod2, ChickWeight, ~ Chick, report = T)