BIFIE.by.RdComputes statistics for user defined functions.
Object of class BIFIEdata
Vector of variables for which statistics should be computed
User defined function. This function must include
a matrix X and a weight vector w as
arguments. The value of this function must be a vector.
An optional vector of parameter names for the
value of userfct.
Optional grouping variable(s)
Optional vector of grouping values. This can be omitted and grouping values will be determined automatically.
Optional logical indicating whether statistical inference based on replication should be employed.
Optional logical indicating whether the user defined function should be evaluated in Rcpp.
Object of class BIFIE.by
Number of digits for rounding output
Further arguments to be passed
A list with following entries
Data frame with statistics defined in userfct
Extensive output with all replicated statistics
More values
survey::svyby
#############################################################################
# EXAMPLE 1: Imputed TIMSS dataset
#############################################################################
data(data.timss1)
data(data.timssrep)
# create BIFIE.dat object
bifieobj <- BIFIEsurvey::BIFIE.data( data.list=data.timss1, wgt=data.timss1[[1]]$TOTWGT,
wgtrep=data.timssrep[, -1 ] )
#****************************
#*** Model 1: Weighted means (as a toy example)
userfct <- function(X,w){
pars <- c( stats::weighted.mean( X[,1], w ),
stats::weighted.mean(X[,2], w ) )
return(pars)
}
res1 <- BIFIEsurvey::BIFIE.by( bifieobj, vars=c("ASMMAT", "migrant", "books"),
userfct=userfct, userparnames=c("MW_MAT", "MW_Migr"),
group="female" )
summary(res1)
# evaluate function in pure R implementation using the use_Rcpp argument
res1b <- BIFIEsurvey::BIFIE.by( bifieobj, vars=c("ASMMAT", "migrant", "books" ),
userfct=userfct, userparnames=c("MW_MAT", "MW_Migr"),
group="female", use_Rcpp=FALSE )
summary(res1b)
#--- statistical inference for a derived parameter (see ?BIFIE.derivedParameters)
# define gender difference for mathematics score (divided by 100)
derived.parameters <- list(
"gender_diff"=~ 0 + I( ( MW_MAT_female1 - MW_MAT_female0 ) / 100 )
)
# inference derived parameter
res1d <- BIFIEsurvey::BIFIE.derivedParameters( res1,
derived.parameters=derived.parameters )
summary(res1d)
if (FALSE) {
#****************************
#**** Model 2: Robust linear model
# (1) start from scratch to formulate the user function for X and w
dat1 <- bifieobj$dat1
vars <- c("ASMMAT", "migrant", "books" )
X <- dat1[,vars]
w <- bifieobj$wgt
library(MASS)
# ASMMAT ~ migrant + books
mod <- MASS::rlm( X[,1] ~ as.matrix( X[, -1 ] ), weights=w )
coef(mod)
# (2) define a user function "my_rlm"
my_rlm <- function(X,w){
mod <- MASS::rlm( X[,1] ~ as.matrix( X[, -1 ] ), weights=w )
return( coef(mod) )
}
# (3) estimate model
res2 <- BIFIEsurvey::BIFIE.by( bifieobj, vars, userfct=my_rlm,
group="female", group_values=0:1)
summary(res2)
# estimate model without computing standard errors
res2a <- BIFIEsurvey::BIFIE.by( bifieobj, vars, userfct=my_rlm,
group="female", se=FALSE)
summary(res2a)
# define a user function with formula language
my_rlm2 <- function(X,w){
colnames(X) <- vars
X <- as.data.frame(X)
mod <- MASS::rlm( ASMMAT ~ migrant + books, weights=w, data=X)
return( coef(mod) )
}
# estimate model
res2b <- BIFIEsurvey::BIFIE.by( bifieobj, vars, userfct=my_rlm2,
group="female", group_values=0:1)
summary(res2b)
#****************************
#**** Model 3: Number of unique values for variables in BIFIEdata
#*** define variables for which the number of unique values should be calculated
vars <- c( "female", "books","ASMMAT" )
#*** define a user function extracting these unqiue values
userfct <- function(X,w){
pars <- apply( X, 2, FUN=function(vv){
length( unique(vv)) } )
# Note that weights are (of course) ignored in this function
return(pars)
}
#*** extract number of unique values
res3 <- BIFIEsurvey::BIFIE.by( bifieobj, vars=vars, userfct=userfct,
userparnames=paste0( vars, "_Nunique"), se=FALSE )
summary(res3)
## Statistical Inference for User Definition Function
## parm Ncases Nweight est
## 1 female_Nunique 4668 78332.99 2.0
## 2 books_Nunique 4668 78332.99 5.0
## 3 ASMMAT_Nunique 4668 78332.99 4613.4
# number of unique values in each of the five imputed datasets
res3$output$parsrepM
## [,1] [,2] [,3] [,4] [,5]
## [1,] 2 2 2 2 2
## [2,] 5 5 5 5 5
## [3,] 4617 4619 4614 4609 4608
#****************************
#**** Model 4: Estimation of a lavaan model with BIFIE.by
#* estimate model in lavaan
data0 <- data.timss1[[1]]
# define lavaan model
lavmodel <- "
ASSSCI ~ likesc
ASSSCI ~~ ASSSCI
likesc ~ female
likesc ~~ likesc
female ~~ female
"
mod0 <- lavaan::lavaan(lavmodel, data=data0, sampling.weights="TOTWGT")
summary(mod0, stand=TRUE, fit.measures=TRUE)
#* construct input for BIFIE.by
vars <- c("ASSSCI","likesc","female","TOTWGT")
X <- data0[,vars]
mod0 <- lavaan::lavaan(lavmodel, data=X, sampling.weights="TOTWGT")
w <- data0$TOTWGT
#* define user function
userfct <- function(X,w){
X1 <- as.data.frame(X)
colnames(X1) <- vars
X1$studwgt <- w
mod0 <- lavaan::lavaan(lavmodel, data=X1, sampling.weights="TOTWGT")
pars <- coef(mod0)
# extract some fit statistics
pars2 <- lavaan::fitMeasures(mod0)
pars <- c(pars, pars2[c("cfi","tli")])
return(pars)
}
#* test function
res0 <- userfct(X,w)
userparnames <- names(res0)
#* estimate lavaan model with replicated sampling weights
res1 <- BIFIEsurvey::BIFIE.by( bifieobj, vars=vars, userfct=userfct,
userparnames=userparnames, use_Rcpp=FALSE )
summary(res1)
}