Compute Influence and Leverage Metrics for objects of type lm

Compute influence and leverage metrics for clustered inference based on the Cluster Jackknife as described in MacKinnon, Nielsen & Webb (2022) for objects of type lm.

Usage

# S3 method for lm
summclust(obj, cluster, params, type = "CRV3", ...)

Arguments

obj: An object of type lm
cluster: A clustering vector
params: A character vector of variables for which leverage statistics should be computed.
type: "CRV3" or "CRV3J" following MacKinnon, Nielsen & Webb. CRV3 by default
...: other function arguments passed to 'vcov'

Value

An object of type summclust, including a CRV3 variance-covariance estimate as described in MacKinnon, Nielsen & Webb (2022)

coef_estimates: The coefficient estimates of the linear model.
vcov: A CRV3 or CRV3J variance-covariance matrix estimate as described in MacKinnon, Nielsen & Webb (2022)
leverage_g: A vector of leverages.
leverage_avg: The cluster leverage.
partial_leverage: The partial leverages.
beta_jack: The jackknifed' leave-on-cluster-out regression coefficients.
params: The input parameter vector 'params'.
N_G: The number of clusters-
call: The summclust() function call.
cluster: The names of the clusters.

References

MacKinnon, James G., Morten Ørregaard Nielsen, and Matthew D. Webb. "Leverage, influence, and the jackknife in clustered regression models: Reliable inference using summclust." arXiv preprint arXiv:2205.03288 (2022).

Examples

# \donttest{
if(requireNamespace("summclust") && requireNamespace("haven")){

library(summclust)
library(haven)

nlswork <- read_dta("http://www.stata-press.com/data/r9/nlswork.dta")
# drop NAs at the moment
nlswork <- nlswork[, c("ln_wage", "grade", "age", "birth_yr", "union", "race", "msp", "ind_code")]
nlswork <- na.omit(nlswork)

lm_fit <- lm(
  ln_wage ~ union +  race + msp + as.factor(birth_yr) + as.factor(age) + as.factor(grade),
  data = nlswork)

res <- summclust(
   obj = lm_fit,
   cluster = ~ind_code,
   params = c("msp", "union")
 )

 summary(res)
 tidy(res)
 plot(res)
}
#> Error in eval(mf, parent.frame()): object 'nlswork' not found
# }