clusterSEs (version 2.2)

cluster.im.glm: Cluster-Adjusted Confidence Intervals And p-Values For GLM

Description

Computes p-values and confidence intervals for GLM models based on cluster-specific model estimation (Ibragimov and Muller 2010). A separate model is estimated in each cluster, and then p-values and confidence intervals are computed based on a t/normal distribution of the cluster-specific estimates.

Usage

cluster.im.glm(mod, dat, cluster, ci.level = 0.95, report = TRUE,
  drop = FALSE, truncate = FALSE)

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?

report

Should a table of results be printed to the console?

drop

Should clusters within which a model cannot be estimated be dropped?

truncate

Should outlying cluster-specific beta estimates be excluded?

Value

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

References

Ibragimov, Rustam, and Ulrich K. Muller. 2010. "t-Statistic Based Correlation and Heterogeneity Robust Inference." Journal of Business & Economic Statistics 28(4): 453-468.

Examples

# example: 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 cluster-adjusted p-values
clust.im.p <- cluster.im.glm(logit.model, WVS, ~ country, report = T)