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Compute Influence and Leverage Metrics

Source: R/summclust.R
summclust.Rd

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

Usage

summclust(obj, ...)

Arguments

obj

An object of class lm or fixest

...

Other arguments

Value

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

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).

See also

summclust.lm, summclust.fixest

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,
   params = c("msp", "union"),
   cluster = ~ind_code,
 )

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