miceadds (version 3.16-18)

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::vcovCL function in the sandwich package.

Usage

lm.cluster(data, formula, cluster, weights=NULL, subset=NULL )
glm.cluster(data, formula, cluster, weights=NULL, subset=NULL, family="gaussian" )
# S3 method for lm.cluster
summary(object,...)
# S3 method for glm.cluster
summary(object,...)
# S3 method for lm.cluster
coef(object,...)
# S3 method for glm.cluster
coef(object,...)
# S3 method for lm.cluster
vcov(object,...)
# S3 method for glm.cluster
vcov(object,...)

Value

List with following entries

lm_res

Value of stats::lm

glm_res

Value of stats::glm

vcov

Covariance matrix of parameter estimates

Arguments

data

Data frame

formula

An R formula

cluster

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

subset

Optional vector specifying a subset of observations to be used.

weights

Optional vector of weights to be used.

family

Description of the error distribution and link function to be used in the model, see stats::glm.

...

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

object

Object of class lm.cluster or glm.cluster

See Also

stats::lm, stats::glm, sandwich::vcovCL

Examples

if (FALSE) {

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

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

#*** Model 1: Linear regression
mod1 <- miceadds::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 <- miceadds::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 <- miceadds::glm.cluster( data=dat, formula=highmath ~ hisei + female,
                cluster="idschool", family="binomial")
coef(mod2)
vcov(mod2)
summary(mod2)

#############################################################################
# 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, method="norm", maxit=3, m=6 )
datlist <- miceadds::mids2datlist( imp )

# linear regression with cluster robust standard errors
mod <- lapply(  datlist, FUN=function(data){
            miceadds::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( miceadds::pool_mi( qhat=betas, u=vars ) )

#------ compute global F-test for hypothesis that all predictors have zero coefficient values
library(mitml)
Nimp <- 6 # number of imputations
np <- length(betas[[1]])   # number of parameters
beta_names <- names(betas[[1]])
# define vector of parameters for which constraints should be tested
constraints <- beta_names[-1]
# create input for mitml::testConstraints function
qhat <- matrix( unlist(betas), ncol=Nimp)
rownames(qhat) <- beta_names
uhat <- array( unlist(vars), dim=c(np,np,Nimp))
dimnames(uhat) <- list( beta_names, beta_names, NULL )
# compute global F-test
Ftest <- mitml::testConstraints( qhat=betas, uhat=vars, constraints=constraints )
print(Ftest)
}