clustSE {CR2} R Documentation

## Cluster robust standard errors with degrees of freedom adjustments (for lm and glm objects)

### Description

Function to compute the CR0, CR1, CR2 cluster robust standard errors (SE) with Bell and McCaffrey (2002) degrees of freedom (df) adjustments. Useful when dealing with datasets with a few clusters. Shows output using different CR types and degrees of freedom choices (for comparative purposes only). For linear and logistic regression models (as well as other GLMs). Computes the BRL-S2 variant.

### Usage

clustSE(mod, clust = NULL, digits = 3, ztest = FALSE)


### Arguments

 mod The lm model object. clust The cluster variable (with quotes). digits Number of decimal places to display. ztest If a normal approximation should be used as the naive degrees of freedom. If FALSE, the between-within degrees of freedom will be used.

### Value

A data frame with the CR adjustments with p-values.

 estimate The regression coefficient. se.unadj The model-based (regular, unadjusted) SE. CR0 Cluster robust SE based on Liang & Zeger (1986). CR1 Cluster robust SE (using an adjustment based on number of clusters). CR2 Cluster robust SE based on Bell and McCaffrey (2002). tCR2 t statistic based on CR2. dfn Degrees of freedom(naive): can be infinite (z) or between-within (default). User specified. dfBM Degrees of freedom based on Bell and McCaffrey (2002). pv.unadj p value based on model-based standard errors. CR0pv p value based on CR0 SE with dfBM. CR0pv.n p value based on CR0 SE with naive df. CR1pv p value based on CR1 SE with dfBM. CR1pv.n p value based on CR1 SE with naive df. CR2pv p value based on CR2 SE with dfBM. CR2pv.n p value based on CR2 SE with naive df.

### References

Bell, R., & McCaffrey, D. (2002). Bias reduction in standard errors for linear regression with multi-stage samples. Survey Methodology, 28, 169-182. (link)

Liang, K.Y., & Zeger, S. L. (1986). Longitudinal data analysis using generalized linear models. Biometrika, 73(1), 13–22. doi: 10.1093/biomet/73.1.13

### Examples

clustSE(lm(mpg ~ am + wt, data = mtcars), 'cyl')
data(sch25)
clustSE(lm(math ~ ses + minority + mses + mhmwk, data = sch25), 'schid')



[Package CR2 version 0.2.1 Index]