miceadds (version 2.10-14)

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, ...)
# 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,...)

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

lm_res

Value of stats::lm

glm_res

Value of stats::glm

vcov

Covariance matrix of parameter estimates

See Also

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

Examples

# NOT RUN {
#############################################################################
# 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)		

# }
# 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 <- 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)
# }