miceadds (version 2.2-0)

lm.cluster: Cluster Robust Standard Errors for Linear Models and General Linear Models

Description

Computes cluster robust standard errors for linear models (stats::lm) and general linear models (stats::glm) using the multiwayvcov::cluster.vcov function in the multiwayvcov package.

Usage

lm.cluster(data, formula, cluster, ...)
glm.cluster(data, formula, cluster, ...)
"summary"(object,...)
"summary"(object,...)
"coef"(object,...)
"coef"(object,...)
"vcov"(object,...)
"vcov"(object,...)

Arguments

data

Data frame

formula

An R formula

cluster

Variable name for cluster variable contained in data or a vector with cluster identifiers

...

Further arguments to be passed to stats::lm and stats::glm

object

Object of class lm.cluster or glm.cluster

Value

List with following entries

See Also

stats::lm, stats::glm, multiwayvcov::cluster.vcov

Examples

#############################################################################
# EXAMPLE 1: Cluster robust standard errors data.ma01
#############################################################################

data(data.ma01)
dat <- data.ma01

#*** Model 1: Linear regression
mod1 <- lm.cluster( data = dat , formula = read ~ hisei + female , 
               cluster = "idschool" )
coef(mod1)
vcov(mod1)
summary(mod1)

# estimate Model 1, but cluster is provided as a vector
mod1b <- lm.cluster( data = dat, formula = read ~ hisei + female, 
                 cluster = dat$idschool)
summary(mod1b)

#*** Model 2: Logistic regression
dat$highmath <- 1 * ( dat$math > 600 )   # create dummy variable
mod2 <- glm.cluster( data = dat , formula = highmath ~ hisei + female , 
                cluster = "idschool" , family="binomial")
coef(mod2)
vcov(mod2)
summary(mod2)		

## Not run: 
# #############################################################################
# # EXAMPLE 2: Cluster robust standard errors for multiply imputed datasets
# #############################################################################
# 
# library(mitools)
# data(data.ma05)
# dat <- data.ma05
# 
# # imputation of the dataset: use six imputations
# resp <- dat[ , - c(1:2) ]
# imp <- mice::mice( resp , imputationMethod="norm" , maxit=3 , m=6 )
# datlist <- mids2datlist( imp )
# 
# # linear regression with cluster robust standard errors
# mod <- lapply(  datlist, FUN = function(data){
#             lm.cluster( data=data , formula=denote ~ migrant+ misei , 
#                     cluster = dat$idclass )
#             }  )
# # extract parameters and covariance matrix
# betas <- lapply( mod , FUN = function(rr){ coef(rr) } )
# vars <- lapply( mod , FUN = function(rr){ vcov(rr) } )
# # conduct statistical inference
# summary(pool_mi( qhat = betas, u = vars )) 
# ## End(Not run)