vcovCR.lm_robust: Cluster-robust variance-covariance matrix for an...

In clubSandwich: Cluster-Robust (Sandwich) Variance Estimators with Small-Sample Corrections

Cluster-robust variance-covariance matrix for an estimatr::lm_robust object.

Description

vcovCR returns a sandwich estimate of the variance-covariance matrix of a set of regression coefficient estimates from an lm_robust object.

Usage

## S3 method for class 'lm_robust'
vcovCR(
  obj,
  cluster,
  type,
  target = NULL,
  inverse_var = NULL,
  form = "sandwich",
  ...
)

Arguments

obj	Fitted model for which to calculate the variance-covariance matrix
cluster	Expression or vector indicating which observations belong to the same cluster. If not specified, will be detected from the clusters argument of obj.
type	Character string specifying which small-sample adjustment should be used, with available options "CR0", "CR1", "CR1p", "CR1S", "CR2", or "CR3". If not specified, will be detected from the se_type argument of obj. See "Details" section of vcovCR for further information.
target	Optional matrix or vector describing the working variance-covariance model used to calculate the CR2 and CR4 adjustment matrices. If a vector, the target matrix is assumed to be diagonal. If not specified, the target is taken to be an identity matrix.
inverse_var	Optional logical indicating whether the weights used in fitting the model are inverse-variance. If not specified, vcovCR will attempt to infer a value.
form	Controls the form of the returned matrix. The default "sandwich" will return the sandwich variance-covariance matrix. Alternately, setting form = "meat" will return only the meat of the sandwich and setting form = B, where B is a matrix of appropriate dimension, will return the sandwich variance-covariance matrix calculated using B as the bread. form = "estfun" will return the (appropriately scaled) estimating function, the transposed crossproduct of which is equal to the sandwich variance-covariance matrix.
...	Additional arguments available for some classes of objects.

Value

An object of class c("vcovCR","clubSandwich"), which consists of a matrix of the estimated variance of and covariances between the regression coefficient estimates.

See Also

vcovCR

Examples

data("ChickWeight", package = "datasets")
ChickWeight$Chick <- factor(ChickWeight$Chick, ordered = FALSE)

if (requireNamespace("estimatr", quietly = TRUE)) withAutoprint({
  library(estimatr)

  lm_fit <- lm_robust(
    weight ~ Time + Diet:Time,
    data = ChickWeight
   )
  vcovCR(lm_fit, cluster = ChickWeight$Chick, type = "CR2")

  lm_fit_clust <- lm_robust(
    weight ~ Time + Diet:Time, data = ChickWeight,
    clusters = Chick
   )
  conf_int(lm_fit_clust, vcov = "CR2")

  # similar model via lm_lin()
  lin_fit_clust <- lm_lin(
    weight ~ Diet, 
    covariates = ~ Time,
    data = ChickWeight,
    clusters = Chick
  )
  conf_int(lin_fit_clust, vcov = "CR2")
  
  lm_fit_fe <- lm_robust(
    weight ~ Time:Diet, data = ChickWeight,
    clusters = Chick,
    fixed_effects = ~ Chick
   )
  vcovCR(lm_fit_fe)
  
  # two-way fixed effects model
  data("MortalityRates")
  MortalityRates <- subset(MortalityRates, cause == "Motor Vehicle")
  MortalityRates$state <- factor(MortalityRates$state)
  MortalityRates$year <- factor(MortalityRates$year)
  MLDA_fit <- lm_robust(
    mrate ~ legal + beertaxa + beerpercap + winepercap + spiritpercap,
    fixed_effects = ~ year + state,
    data = MortalityRates,
    cluster = state
  )
  conf_int(MLDA_fit, vcov = "CR2")

  if (requireNamespace("plm", quietly = TRUE)) withAutoprint({

    data("Produc", package = "plm")
    lm_individual <- lm_robust(
      log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp,
      data = Produc,
      fixed_effects = ~ state,
      cluster = state
     )
    vcovCR(lm_individual, type = "CR2")

  })

})

clubSandwich documentation built on May 4, 2026, 9:09 a.m.