BIFIE.by: Statistics for User Defined Functions

In BIFIEsurvey: Tools for Survey Statistics in Educational Assessment

Statistics for User Defined Functions

Description

Computes statistics for user defined functions.

Usage

BIFIE.by( BIFIEobj, vars, userfct, userparnames=NULL,
     group=NULL, group_values=NULL, se=TRUE, use_Rcpp=TRUE)

## S3 method for class 'BIFIE.by'
summary(object,digits=4,...)

## S3 method for class 'BIFIE.by'
coef(object,...)

## S3 method for class 'BIFIE.by'
vcov(object,...)

Arguments

BIFIEobj	Object of class BIFIEdata
vars	Vector of variables for which statistics should be computed
userfct	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.
userparnames	An optional vector of parameter names for the value of userfct.
group	Optional grouping variable(s)
group_values	Optional vector of grouping values. This can be omitted and grouping values will be determined automatically.
se	Optional logical indicating whether statistical inference based on replication should be employed.
use_Rcpp	Optional logical indicating whether the user defined function should be evaluated in Rcpp.
object	Object of class BIFIE.by
digits	Number of digits for rounding output
...	Further arguments to be passed

Value

A list with following entries

stat	Data frame with statistics defined in userfct
output	Extensive output with all replicated statistics
...	More values

See Also

survey::svyby

Examples

#############################################################################
# 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)

## Not run: 
#****************************
#**** 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)

## End(Not run)

BIFIEsurvey documentation built on April 5, 2022, 1:14 a.m.