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

Description

Computes p-values and confidence intervals for multinomial logit 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.mlogit(mod, dat, cluster, ci.level = 0.95, report = TRUE,
  truncate = FALSE, return.vcv = FALSE)

Arguments

mod

A model estimated using mlogit.

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?

truncate

Should outlying cluster-specific beta estimates be excluded?

return.vcv

Should a VCV matrix and the means of cluster-specific coefficient estimates be returned?

Value

A list with the elements

p.values

A matrix of the estimated p-values.

A matrix of confidence intervals.

References

Esarey, Justin, and Andrew Menger. 2017. "Practical and Effective Approaches to Dealing with Clustered Data." Political Science Research and Methods forthcoming: 1-35. <URL:http://jee3.web.rice.edu/cluster-paper.pdf>.

Ibragimov, Rustam, and Ulrich K. Muller. 2010. "t-Statistic Based Correlation and Heterogeneity Robust Inference." Journal of Business & Economic Statistics 28(4): 453-468. <DOI:10.1198/jbes.2009.08046>.

Examples

Run this code

# NOT RUN {
# example: predict type of heating system installed in house
require(mlogit)
data("Heating", package = "mlogit")
H <- Heating
H.ml <- mlogit.data(H, shape="wide", choice="depvar", varying=c(3:12))
m <- mlogit(depvar~ic+oc, H.ml)

# compute cluster-adjusted p-values
cluster.im.h <- cluster.im.mlogit(m, H.ml, ~ region)

# }

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