Package 'BIFIEsurvey'

Title: Tools for Survey Statistics in Educational Assessment
Description: Contains tools for survey statistics (especially in educational assessment) for datasets with replication designs (jackknife, bootstrap, replicate weights; see Kolenikov, 2010; Pfefferman & Rao, 2009a, 2009b, <doi:10.1016/S0169-7161(09)70003-3>, <doi:10.1016/S0169-7161(09)70037-9>); Shao, 1996, <doi:10.1080/02331889708802523>). Descriptive statistics, linear and logistic regression, path models for manifest variables with measurement error correction and two-level hierarchical regressions for weighted samples are included. Statistical inference can be conducted for multiply imputed datasets and nested multiply imputed datasets and is in particularly suited for the analysis of plausible values (for details see George, Oberwimmer & Itzlinger-Bruneforth, 2016; Bruneforth, Oberwimmer & Robitzsch, 2016; Robitzsch, Pham & Yanagida, 2016). The package development was supported by BIFIE (Federal Institute for Educational Research, Innovation and Development of the Austrian School System; Salzburg, Austria).
Authors: BIFIE [aut], Alexander Robitzsch [aut, cre], Konrad Oberwimmer [aut]
Maintainer: Alexander Robitzsch <[email protected]>
License: GPL (>= 2)
Version: 3.6-2
Built: 2024-03-19 07:27:58 UTC
Source: https://github.com/alexanderrobitzsch/bifiesurvey

Help Index


Tools for Survey Statistics in Educational Assessment

Description

Contains tools for survey statistics (especially in educational assessment) for datasets with replication designs (jackknife, bootstrap, replicate weights; see Kolenikov, 2010; Pfefferman & Rao, 2009a, 2009b, <doi:10.1016/S0169-7161(09)70003-3>, <doi:10.1016/S0169-7161(09)70037-9>); Shao, 1996, <doi:10.1080/02331889708802523>). Descriptive statistics, linear and logistic regression, path models for manifest variables with measurement error correction and two-level hierarchical regressions for weighted samples are included. Statistical inference can be conducted for multiply imputed datasets and nested multiply imputed datasets and is in particularly suited for the analysis of plausible values (for details see George, Oberwimmer & Itzlinger-Bruneforth, 2016; Bruneforth, Oberwimmer & Robitzsch, 2016; Robitzsch, Pham & Yanagida, 2016). The package development was supported by BIFIE (Federal Institute for Educational Research, Innovation and Development of the Austrian School System; Salzburg, Austria).

Details

The BIFIEsurvey package include basic descriptive functions for large scale assessment data to complement the more comprehensive survey package. The functions in this package were written in Rcpp.

The features of BIFIEsurvey include for designs with replicate weights (which includes Jackknife and Bootstrap as general approaches):

Author(s)

BIFIE [aut], Alexander Robitzsch [aut, cre], Konrad Oberwimmer [aut]

Maintainer: Alexander Robitzsch <[email protected]>

References

Bruneforth, M., Oberwimmer, K., & Robitzsch, A. (2016). Reporting und Analysen. In S. Breit & C. Schreiner (Hrsg.). Large-Scale Assessment mit R: Methodische Grundlagen der oesterreichischen Bildungsstandardueberpruefung (S. 333-362). Wien: facultas.

George, A. C., Oberwimmer, K., & Itzlinger-Bruneforth, U. (2016). Stichprobenziehung. In S. Breit & C. Schreiner (Hrsg.). Large-Scale Assessment mit R: Methodische Grundlagen der oesterreichischen Bildungsstandardueberpruefung (S. 51-81). Wien: facultas.

Kolenikov, S. (2010). Resampling variance estimation for complex survey data. Stata Journal, 10(2), 165-199.

Pfefferman, D., & Rao, C. R. (2009a). Handbook of statistics, Vol. 29A: Sample surveys: Design, methods and applications. Amsterdam: North Holland.

Pfefferman, D., & Rao, C. R. (2009b). Handbook of statistics, Vol. 29B: Sample surveys: Inference and analysis. Amsterdam: North Holland.

Robitzsch, A., Pham, G., & Yanagida, T. (2016). Fehlende Daten und Plausible Values. In S. Breit & C. Schreiner (Hrsg.). Large-Scale Assessment mit R: Methodische Grundlagen der oesterreichischen Bildungsstandardueberpruefung (S. 259-293). Wien: facultas.

Shao, J. (1996). Invited discussion paper: Resampling methods in sample surveys. Statistics, 27(3-4), 203-237.

See Also

See also the survey, intsvy, EdSurvey, lavaan.survey, EVER and the eatRep packages.

Examples

##   |-----------------------------------------------------------------
##   | BIFIEsurvey 0.1-21 (2014-06-21)
##   | Maintainer: Alexander Robitzsch <a.robitzsch at bifie.at >
##   | http://www.bifie.at
##   |-----------------------------------------------------------------

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Conversion and Selection of BIFIEdata Objects

Description

Functions for converting and selecting objects of class BIFIEdata. The function BIFIE.BIFIEdata2BIFIEcdata converts the BIFIEdata objects in a non-compact form (cdata=FALSE) into an object of class BIFIEdata in a compact form (cdata=TRUE). The function BIFIE.BIFIE2data2BIFIEdata takes the reverse operation.

The function BIFIE.BIFIEdata2datalist converts a (part) of the object of class BIFIEdata into a list of multiply-imputed datasets.

Usage

BIFIE.BIFIEdata2BIFIEcdata(bifieobj, varnames=NULL, impdata.index=NULL)

BIFIE.BIFIEcdata2BIFIEdata(bifieobj, varnames=NULL, impdata.index=NULL)

BIFIE.BIFIEdata2datalist(bifieobj, varnames=NULL, impdata.index=NULL,
    as_data_frame=FALSE)

Arguments

bifieobj

Object of class BIFIEdata

varnames

Variables chosen for the selection

impdata.index

Selected indices of imputed datasets

as_data_frame

Logical indicating whether list of length one should be converted into a data frame

Value

An object of class BIFIEdata saved in a non-compact or compact way, see value cdata.

See Also

BIFIE.data

Examples

#############################################################################
# EXAMPLE 1: BIFIEdata conversions using data.timss1 dataset
#############################################################################
data(data.timss1)
data(data.timssrep)

# create BIFIEdata object
bdat1 <- BIFIEsurvey::BIFIE.data( data.list=data.timss1, wgt=data.timss1[[1]]$TOTWGT,
            wgtrep=data.timssrep[, -1 ])
summary(bdat1)

# convert BIFIEdata object bdat1 into a BIFIEcdata object with
#  only using the first three datasets and a variable selection
bdat2 <- BIFIEsurvey::BIFIE.BIFIEdata2BIFIEcdata( bifieobj=bdat1,
                varnames=bdat1$varnames[ c(1:7,10) ] )

# convert bdat2 into BIFIEdata object and only use the first three imputed datasets
bdat3 <- BIFIEsurvey::BIFIE.BIFIEcdata2BIFIEdata( bifieobj=bdat2, impdata.index=1:3)

# object summaries
summary(bdat1)
summary(bdat2)
summary(bdat3)

## Not run: 
#############################################################################
# EXAMPLE 2: Extract unique elements in BIFIEdata object
#############################################################################

data(data.timss1)
data(data.timssrep)

# create BIFIEdata object
bifieobj <- BIFIEsurvey::BIFIE.data( data.list=data.timss1, wgt=data.timss1[[1]]$TOTWGT,
            wgtrep=data.timssrep[, -1 ])
summary(bifieobj)

# define variables for which unique values should be extracted
vars <- c( "female", "books","ASMMAT" )
# convert these variables from BIFIEdata object into a list of datasets
bdatlist <- BIFIEsurvey::BIFIE.BIFIEdata2datalist( bifieobj, varnames=vars )
# look for unique values in first dataset for variables
values <- lapply( bdatlist[[1]], FUN=function(vv){
                sort( unique( vv ) ) } )
# number of unique values in first dataset
Nvalues <- lapply( bdatlist[[1]], FUN=function(vv){
                length( unique( vv ) ) } )
# number of unique values in all datasets
Nvalues2 <- lapply( vars, FUN=function(vv){
    #vv <- vars[1]
    unlist( lapply( bdatlist, FUN=function(dd){
                length( unique( dd[,vv]  ) )
                        }    )     )
                    } )
# --> for extracting the number of unique values using BIFIE.by and a user
#     defined function see Example 1, Model 3 in "BIFIE.by"

## End(Not run)

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)

Correlations and Covariances

Description

Computes correlations and covariances

Usage

BIFIE.correl(BIFIEobj, vars, group=NULL, group_values=NULL, se=TRUE)

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

## S3 method for class 'BIFIE.correl'
coef(object,type=NULL, ...)

## S3 method for class 'BIFIE.correl'
vcov(object,type=NULL, ...)

Arguments

BIFIEobj

Object of class BIFIEdata

vars

Vector of variables for which statistics should be computed

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.

object

Object of class BIFIE.correl

digits

Number of digits for rounding output

type

If type="cov", then covariances instead of correlations are extracted.

...

Further arguments to be passed

Value

A list with following entries

stat.cor

Data frame with correlation statistics

stat.cov

Data frame with covariance statistics

cor_matrix

List of estimated correlation matrices

cov_matrix

List of estimated covariance matrices

output

Extensive output with all replicated statistics

...

More values

See Also

stats::cov.wt, intsvy::timss.rho, intsvy::timss.rho.pv, Hmisc::rcorr, miceadds::ma.wtd.corNA

Examples

#############################################################################
# EXAMPLE 1: Imputed TIMSS dataset
#############################################################################

data(data.timss1)
data(data.timssrep)

# create BIFIE.dat object
bdat <- BIFIEsurvey::BIFIE.data( data.list=data.timss1, wgt=data.timss1[[1]]$TOTWGT,
           wgtrep=data.timssrep[, -1 ] )

# Correlations splitted by gender
res1 <- BIFIEsurvey::BIFIE.correl( bdat, vars=c("lang", "books", "migrant" ),
              group="female", group_values=0:1 )
summary(res1)

# Correlations splitted by gender: no statistical inference (se=FALSE)
res1a <- BIFIEsurvey::BIFIE.correl( bdat, vars=c("lang", "books", "migrant" ),
              group="female", group_values=0:1, se=FALSE)
summary(res1a)

Cross Tabulation

Description

Creates cross tabulations and computes some effect sizes.

Usage

BIFIE.crosstab( BIFIEobj, vars1, vars2, vars_values1=NULL, vars_values2=NULL,
     group=NULL, group_values=NULL, se=TRUE )

## S3 method for class 'BIFIE.crosstab'
summary(object,digits=3,...)

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

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

Arguments

BIFIEobj

Object of class BIFIEdata

vars1

Row variable

vars2

Column variable

vars_values1

Optional vector of values of variable vars1

vars_values2

Optional vector of values of variable vars2

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.

object

Object of class BIFIE.univar

digits

Number of digits for rounding output

...

Further arguments to be passed

Value

A list with following entries

stat.probs

Statistics for joint and conditional probabilities

stat.marg

Statistics for marginal probabilities

stat.es

Statistics for effect sizes ww (based on χ2\chi^2), Cramers VV, Goodman's gamma, the PRE lambda measure and Kruskals tau.

output

Extensive output with all replicated statistics

...

More values

See Also

survey::svytable, Hmisc::wtd.table

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: cross tabulation
res1 <- BIFIEsurvey::BIFIE.crosstab( bifieobj, vars1="migrant",
               vars2="books", group="female" )
summary(res1)

Creates an Object of Class BIFIEdata

Description

This function creates an object of class BIFIEdata. Finite sampling correction of statistical inferences can be conducted by specifying appropriate input in the fayfac argument.

Usage

BIFIE.data(data.list, wgt=NULL, wgtrep=NULL, fayfac=1, pv_vars=NULL,
     pvpre=NULL, cdata=FALSE, NMI=FALSE)

## S3 method for class 'BIFIEdata'
summary(object,...)

## S3 method for class 'BIFIEdata'
print(x,...)

Arguments

data.list

List of multiply imputed datasets. Can be also a list of list of imputed datasets in case of nested multiple imputation. Then, the argument NMI=TRUE must be specified.

wgt

A string indicating the label of case weight or a vector containing all case weights.

wgtrep

Optional vector of replicate weights

fayfac

Fay factor for calculating standard errors, a numeric value. If finite sampling correction is requested, an appropriate vector input can be used (see Example 3).

pv_vars

Optional vector for names of plausible values, see BIFIE.data.jack.

pvpre

Optional vector for prefixes of plausible values, see BIFIE.data.jack.

cdata

An optional logical indicating whether the BIFIEdata object should be compactly saved. The default is FALSE.

NMI

Optional logical indicating whether data.list is obtained by nested multiple imputation.

object

Object of class BIFIEdata

x

Object of class BIFIEdata

...

Further arguments to be passed

Value

An object of class BIFIEdata saved in a non-compact or compact way, see value cdata. The following entries are included in the list:

datalistM

Stacked list of imputed datasets (if cdata=FALSE)

wgt

Vector with case weights

wgtrep

Matrix with replicate weights

Nimp

Number of imputed datasets

N

Number of observations in a dataset

dat1

Last imputed dataset

varnames

Vector with variable names

fayfac

Fay factor.

RR

Number of replicate weights

NMI

Logical indicating whether the dataset is nested multiply imputed.

cdata

Logical indicating whether the BIFIEdata object is in compact format (cdata=TRUE) or in a non-compact format (cdata=FALSE).

Nvars

Number of variables

variables

Data frame including some informations about variables. All transformations are saved in the column source.

datalistM_ind

Data frame with response indicators (if cdata=TRUE)

datalistM_imputed

Data frame with imputed values (if cdata=TRUE)

See Also

See BIFIE.data.transform for data transformations on BIFIEdata objects.

For saving and loading BIFIEdata objects see save.BIFIEdata.

For converting PIRLS/TIMSS or PISA datasets into BIFIEdata objects see BIFIE.data.jack.

See the BIFIEdata2svrepdesign function for converting BIFIEdata objects to objects used in the survey package.

Examples

#############################################################################
# EXAMPLE 1: Create BIFIEdata object with multiply-imputed TIMSS data
#############################################################################
data(data.timss1)
data(data.timssrep)

bdat <- BIFIEsurvey::BIFIE.data( data.list=data.timss1, wgt=data.timss1[[1]]$TOTWGT,
            wgtrep=data.timssrep[, -1 ] )
summary(bdat)
# create BIFIEdata object in a compact way
bdat2 <- BIFIEsurvey::BIFIE.data( data.list=data.timss1, wgt=data.timss1[[1]]$TOTWGT,
            wgtrep=data.timssrep[, -1 ], cdata=TRUE)
summary(bdat2)

## Not run: 
#############################################################################
# EXAMPLE 2: Create BIFIEdata object with one dataset
#############################################################################
data(data.timss2)

# use first dataset with missing data from data.timss2
bdat <- BIFIEsurvey::BIFIE.data( data.list=data.timss2[[1]], wgt=data.timss2[[1]]$TOTWGT)

## End(Not run)

#############################################################################
# EXAMPLE 3: BIFIEdata objects with finite sampling correction
#############################################################################

data(data.timss1)
data(data.timssrep)

#-----
# BIFIEdata object without finite sampling correction
bdat1 <- BIFIEsurvey::BIFIE.data( data.list=data.timss1, wgt=data.timss1[[1]]$TOTWGT,
            wgtrep=data.timssrep[, -1 ] )
summary(bdat1)

#-----
# generate BIFIEdata object with finite sampling correction by adjusting
# the "fayfac" factor
bdat2 <- bdat1
#-- modify "fayfac" constant
fayfac0 <- bdat1$fayfac
# set fayfac=.75 for the first 50 replication zones (25% of students in the
# population were sampled) and fayfac=.20 for replication zones 51-75
# (meaning that 80% of students were sampled)
fayfac <- rep( fayfac0, bdat1$RR )
fayfac[1:50] <- fayfac0 * .75
fayfac[51:75] <- fayfac0 * .20
# include this modified "fayfac" factor in bdat2
bdat2$fayfac <- fayfac
summary(bdat2)
summary(bdat1)

#---- compare some univariate statistics
# no finite sampling correction
res1 <- BIFIEsurvey::BIFIE.univar( bdat1, vars="ASMMAT")
summary(res1)
# finite sampling correction
res2 <- BIFIEsurvey::BIFIE.univar( bdat2, vars="ASMMAT")
summary(res2)

## Not run: 
#############################################################################
# EXAMPLE 4: Create BIFIEdata object with nested multiply imputed dataset
#############################################################################

data(data.timss4)
data(data.timssrep)

# nested imputed dataset, save it in compact format
bdat <- BIFIEsurvey::BIFIE.data( data.list=data.timss4,
            wgt=data.timss4[[1]][[1]]$TOTWGT, wgtrep=data.timssrep[, -1 ],
            NMI=TRUE, cdata=TRUE )
summary(bdat)

## End(Not run)

Create BIFIE.data Object based on Bootstrap

Description

Creates a BIFIE.data object based on bootstrap designs. The sampling is done assuming independence of cases.

Usage

BIFIE.data.boot( data, wgt=NULL,  pv_vars=NULL,
         Nboot=500, seed=.Random.seed, cdata=FALSE)

Arguments

data

Data frame: Can be a single or a list of multiply imputed datasets

wgt

A string indicating the label of case weight.

pv_vars

An optional vector of plausible values which define multiply imputed datasets.

Nboot

Number of bootstrap samples for usage

seed

Simulation seed.

cdata

An optional logical indicating whether the BIFIEdata object should be compactly saved. The default is FALSE.

Value

Object of class BIFIEdata

See Also

BIFIE.data, BIFIE.data.jack

Examples

## Not run: 
#############################################################################
# EXAMPLE 1: Bootstrap TIMSS data set
#############################################################################
data(data.timss1)

# bootstrap samples using weights
bifieobj1 <- BIFIEsurvey::BIFIE.data.boot( data.timss1, wgt="TOTWGT" )
summary(bifieobj1)

# bootstrap samples without weights
bifieobj2 <- BIFIEsurvey::BIFIE.data.boot( data.timss1  )
summary(bifieobj2)

## End(Not run)

Create BIFIE.data Object with Jackknife Zones

Description

Creates a BIFIE.data object for designs with jackknife zones, especially for TIMSS/PIRLS and PISA studies.

Usage

BIFIE.data.jack(data, wgt=NULL, jktype="JK_TIMSS", pv_vars=NULL,
     jkzone=NULL, jkrep=NULL, jkfac=NULL, fayfac=NULL,
     wgtrep="W_FSTR", pvpre=paste0("PV",1:5), ngr=100,
     seed=.Random.seed, cdata=FALSE)

Arguments

data

Data frame: Can be a single or a list of multiply-imputed datasets

wgt

A string indicating the label of case weight. In case of jktype="JK_TIMSS" the weight is specified as wgt="TOTWGT" as the default.

pv_vars

An optional vector of plausible values which define multiply-imputed datasets.

jktype

Type of jackknife procedure for creating the BIFIE.data object. jktype="JK_TIMSS" refers to TIMSS/PIRLS datasets up to 2011 data, jktype="JK_TIMSS2" refers to TIMSS/PIRLS datasets starting from 2015 data. The type "JK_GROUP" creates jackknife weights based on a user defined grouping, the type "JK_RANDOM" creates random groups. The number of random groups can be defined in ngr. The argument jktype="RW_PISA" converts PISA datasets into objects of class BIFIEdata.

jkzone

Jackknife zones. If jktype="JK_TIMSS", then jkzone="JKZONE".

jkrep

Jackknife replicate factors. If jktype="JK_TIMSS", then jkrep="JKREP".

jkfac

Factor for multiplying jackknife replicate weights. If jktype="JK_TIMSS", then jkfac=2.

fayfac

Fay factor for statistical inference. The default is set to NULL.

wgtrep

Variables in the dataset which refer to the replicate weights. In case of cdata=TRUE, the replicate weights are deleted from datalistM.

pvpre

Only applicable for jktype="RW_PISA". The vector contains the prefixes of the variables containing plausible values.

ngr

Number of randomly created groups in "JK_RANDOM".

seed

The simulation seed if "JK_RANDOM" is chosen. If seed=NULL, then the grouping is done according the order in the dataset.

cdata

An optional logical indicating whether the BIFIEdata object should be compactly saved. The default is FALSE.

Value

Object of class BIFIEdata

See Also

BIFIE.data, BIFIE.data.boot

Examples

#############################################################################
# EXAMPLE 1: Convert TIMSS dataset to BIFIE.data object
#############################################################################

data(data.timss3)

# define plausible values
pv_vars <- c("ASMMAT", "ASSSCI" )
# create BIFIE.data objects -> 5 imputed datasets
bdat1 <- BIFIEsurvey::BIFIE.data.jack( data=data.timss3,  pv_vars=pv_vars,
             jktype="JK_TIMSS"  )
summary(bdat1)

# create BIFIE.data objects -> all PVs are included in one dataset
bdat2 <- BIFIEsurvey::BIFIE.data.jack( data=data.timss3,  jktype="JK_TIMSS"  )
summary(bdat2)

#############################################################################
# EXAMPLE 2: Creation of Jackknife zones and replicate weights for data.test1
#############################################################################

data(data.test1)

# create jackknife zones based on random group creation
bdat1 <- BIFIEsurvey::BIFIE.data.jack( data=data.test1,  jktype="JK_RANDOM",
                    ngr=50 )
summary(bdat1)
stat1 <- BIFIEsurvey::BIFIE.univar( bdat1, vars="math",  group="stratum" )
summary(stat1)

# random creation of groups and inclusion of weights
bdat2 <- BIFIEsurvey::BIFIE.data.jack( data=data.test1,  jktype="JK_RANDOM",
                ngr=75, seed=987, wgt="wgtstud")
summary(bdat2)
stat2 <- BIFIEsurvey::BIFIE.univar( bdat2, vars="math",  group="stratum" )
summary(stat2)

# using idclass as jackknife zones
bdat3 <- BIFIEsurvey::BIFIE.data.jack( data=data.test1,  jktype="JK_GROUP",
                jkzone="idclass", wgt="wgtstud")
summary(bdat3)
stat3 <- BIFIEsurvey::BIFIE.univar( bdat3, vars="math",  group="stratum" )
summary(stat3)

# create BIFIEdata object with a list of imputed datasets
dataList <- list( data.test1, data.test1, data.test1 )
bdat4 <- BIFIEsurvey::BIFIE.data.jack( data=dataList,  jktype="JK_GROUP",
                jkzone="idclass", wgt="wgtstud")
summary(bdat4)

## Not run: 
#############################################################################
# EXAMPLE 3: Converting a PISA dataset into a BIFIEdata object
#############################################################################

data(data.pisaNLD)

# BIFIEdata with cdata=FALSE
bifieobj <- BIFIEsurvey::BIFIE.data.jack( data.pisaNLD, jktype="RW_PISA", cdata=FALSE)
summary(bifieobj)
# BIFIEdata with cdata=TRUE
bifieobj1 <- BIFIEsurvey::BIFIE.data.jack( data.pisaNLD, jktype="RW_PISA", cdata=TRUE)
summary(bifieobj1)

## End(Not run)

Data Transformation for BIFIEdata Objects

Description

Computes a data transformation for BIFIEdata objects.

Usage

BIFIE.data.transform( bifieobj, transform.formula, varnames.new=NULL )

Arguments

bifieobj

Object of class BIFIEdata

transform.formula

R formula object for data transformation.

varnames.new

Optional vector of names for new defined variables.

Value

An object of class BIFIEdata. Additional values are

varnames.added

Added variables in data transformation

varsindex.added

Indices of added variables

Examples

library(miceadds)

#############################################################################
# EXAMPLE 1: Data transformations for TIMSS data
#############################################################################

data(data.timss2)
data(data.timssrep)
# create BIFIEdata object
bifieobj1 <- BIFIEsurvey::BIFIE.data( data.timss2, wgt=data.timss2[[1]]$TOTWGT,
            wgtrep=data.timssrep[,-1] )
# create BIFIEdata object in compact way (cdata=TRUE)
bifieobj2 <- BIFIEsurvey::BIFIE.data( data.timss2, wgt=data.timss2[[1]]$TOTWGT,
            wgtrep=data.timssrep[,-1], cdata=TRUE)

#****************************
#*** Transformation 1: Squared and cubic book variable
transform.formula <- ~ I( books^2 ) + I( books^3 )
# as.character(transform.formula)
bifieobj <- BIFIEsurvey::BIFIE.data.transform( bifieobj1,
                  transform.formula=transform.formula)
bifieobj$variables
# rename added variables
bifieobj$varnames[ bifieobj$varsindex.added ] <- c("books_sq", "books_cub")

# check descriptive statistics
res1 <- BIFIEsurvey::BIFIE.univar( bifieobj, vars=c("books_sq", "books_cub" ) )
summary(res1)

## Not run: 
#****************************
#*** Transformation 2: Create dummy variables for variable book
transform.formula <- ~ as.factor(books)
bifieobj <- BIFIEsurvey::BIFIE.data.transform( bifieobj,
                    transform.formula=transform.formula )
##   Included 5 variables: as.factor(books)1 as.factor(books)2 as.factor(books)3
##        as.factor(books)4 as.factor(books)5
bifieobj$varnames[ bifieobj$varsindex.added ] <- paste0("books_D", 1:5)

#****************************
#*** Transformation 3: Discretized mathematics score
hi3a <- BIFIEsurvey::BIFIE.hist( bifieobj, vars="ASMMAT" )
plot(hi3a)
transform.formula <- ~ I( as.numeric(cut( ASMMAT, breaks=seq(200,800,100) )) )
bifieobj <- BIFIEsurvey::BIFIE.data.transform( bifieobj,
                 transform.formula=transform.formula, varnames.new="ASMMAT_discret")
hi3b <- BIFIEsurvey::BIFIE.hist( bifieobj, vars="ASMMAT_discret", breaks=1:7 )
plot(hi3b)
# check frequencies
fr3b <- BIFIEsurvey::BIFIE.freq( bifieobj, vars="ASMMAT_discret", se=FALSE )
summary(fr3b)

#****************************
#*** Transformation 4: include standardization variables for book variable

# start with testing the transformation function on a single dataset
dat1 <- bifieobj$dat1
stats::weighted.mean( dat1[,"books"], dat1[,"TOTWGT"], na.rm=TRUE)
sqrt( Hmisc::wtd.var( dat1[,"books"], dat1[,"TOTWGT"], na.rm=TRUE) )
# z standardization
transform.formula <- ~ I( ( books - weighted.mean( books, TOTWGT, na.rm=TRUE) )/
                                sqrt( Hmisc::wtd.var( books, TOTWGT, na.rm=TRUE) ))
bifieobj <- BIFIEsurvey::BIFIE.data.transform( bifieobj,
               transform.formula=transform.formula, varnames.new="z_books" )
# standardize variable books with M=500 and SD=100
transform.formula <- ~ I(
        500 + 100*( books - stats::weighted.mean( books, w=TOTWGT, na.rm=TRUE) ) /
              sqrt( Hmisc::wtd.var( books, weights=TOTWGT, na.rm=TRUE) )  )
bifieobj <- BIFIEsurvey::BIFIE.data.transform( bifieobj,
             transform.formula=transform.formula, varnames.new="z500_books" )

# standardize variable books with respect to M and SD of ALL imputed datasets
res <- BIFIEsurvey::BIFIE.univar( bifieobj, vars="books" )
summary(res)
##       var  Nweight Ncases     M M_SE M_fmi M_VarMI M_VarRep    SD SD_SE SD_fmi
##   1 books 76588.72   4554 2.945 0.04     0       0    0.002 1.146 0.015      0
M <- round(res$output$mean1,5)
SD <- round(res$output$sd1,5)
transform.formula <- paste0( " ~ I( ( books - ",  M, " ) / ", SD, ")"  )
##   > transform.formula
##   [1] " ~ I( ( books - 2.94496 ) / 1.14609)"
bifieobj <- BIFIEsurvey::BIFIE.data.transform( bifieobj,
                 transform.formula=stats::as.formula(transform.formula),
                 varnames.new="zall_books" )

# check statistics
res4 <- BIFIEsurvey::BIFIE.univar( bifieobj,
              vars=c("z_books", "z500_books", "zall_books") )
summary(res4)

#****************************
#*** Transformation 5: include rank transformation for variable ASMMAT

# calculate percentage ranks using wtd.rank function from Hmisc package
dat1 <- bifieobj$dat1
100 * Hmisc::wtd.rank( dat1[,"ASMMAT"], w=dat1[,"TOTWGT"] ) / sum( dat1[,"TOTWGT"] )
# define an auxiliary function for calculating percentage ranks
wtd.percrank <- function( x, w ){
    100 * Hmisc::wtd.rank( x, w, na.rm=TRUE ) / sum( w, na.rm=TRUE )
}
wtd.percrank( dat1[,"ASMMAT"], dat1[,"TOTWGT"] )
# define transformation formula
transform.formula <- ~ I( wtd.percrank( ASMMAT, TOTWGT ) )
# add ranks to BIFIEdata object
bifieobj <- BIFIEsurvey::BIFIE.data.transform( bifieobj,
               transform.formula=transform.formula,  varnames.new="ASMMAT_rk")
# check statistic
res5 <- BIFIEsurvey::BIFIE.univar( bifieobj, vars=c("ASMMAT_rk" ) )
summary(res5)

#****************************
#*** Transformation 6: recode variable books

library(car)
# recode variable books according to "1,2=0, 3,4=1, 5=2"
dat1 <- bifieobj$dat1
# use Recode function from car package
car::Recode( dat1[,"books"], "1:2='0'; c(3,4)='1';5='2'")
# define transformation formula
transform.formula <- ~ I( car::Recode( books, "1:2='0'; c(3,4)='1';5='2'") )
bifieobj <- BIFIEsurvey::BIFIE.data.transform( bifieobj,
               transform.formula=transform.formula,  varnames.new="book_rec" )
res6 <- BIFIEsurvey::BIFIE.freq( bifieobj, vars=c("book_rec" ) )
summary(res6)

#****************************
#*** Transformation 7: include some variables aggregated to the school level

dat1 <- as.data.frame(bifieobj$dat1)
# at first, create school ID in the dataset by transforming the student ID
dat1$idschool <- as.numeric(substring( dat1$IDSTUD, 1, 5 ))
transform.formula <- ~ I( as.numeric( substring( IDSTUD, 1, 5 ) ) )
bifieobj <- BIFIEsurvey::BIFIE.data.transform( bifieobj,
               transform.formula=transform.formula, varnames.new="idschool" )

#*** test function for a single dataset bifieobj$dat1
dat1 <- as.data.frame(bifieobj$dat1)
gm <- miceadds::GroupMean( data=dat1$ASMMAT, group=dat1$idschool, extend=TRUE)[,2]

# add school mean ASMMAT
tformula <- ~ I( miceadds::GroupMean( ASMMAT, group=idschool, extend=TRUE)[,2] )
bifieobj <- BIFIEsurvey::BIFIE.data.transform( bifieobj, transform.formula=tformula,
                varnames.new="M_ASMMAT" )
# add within group centered mathematics values of ASMMAT
bifieobj <- BIFIEsurvey::BIFIE.data.transform( bifieobj,
                transform.formula=~ 0 + I( ASMMAT - M_ASMMAT ),
                varnames.new="WC_ASMMAT" )

# add school mean books
bifieobj <- BIFIEsurvey::BIFIE.data.transform( bifieobj,
                transform.formula=~ 0 + I( add.groupmean( books, idschool ) ),
                varnames.new="M_books" )

#****************************
#*** Transformation 8: include fitted values and residuals from a linear model
# create new BIFIEdata object
data(data.timss1)
bifieobj3 <- BIFIEsurvey::BIFIE.data( data.timss1, wgt=data.timss1[[1]]$TOTWGT,
            wgtrep=data.timssrep[,-1] )

# specify transformation
transform.formula <- ~ I( fitted( stats::lm( ASMMAT ~ migrant + female ) ) ) +
                             I( residuals( stats::lm( ASMMAT ~ migrant + female ) ) )
  # Note that lm omits cases in regression by listwise deletion.
# add fitted values and residual to BIFIEdata object
bifieobj <- BIFIEsurvey::BIFIE.data.transform( bifieobj3,
                  transform.formula=transform.formula )
bifieobj$varnames[ bifieobj$varsindex.added ] <- c("math_fitted1", "math_resid1")

#****************************
#*** Transformation 9: Including principal component scores in BIFIEdata object

# define auxiliary function for extracting PCA scores
BIFIE.princomp <- function( formula, Ncomp ){
    X <- stats::princomp( formula, cor=TRUE)
    Xp <- X$scores[, 1:Ncomp ]
    return(Xp)
}
# define transformation formula
transform.formula <- ~ I( BIFIE.princomp( ~ migrant + female + books + lang + ASMMAT, 3 ))
# apply transformation
bifieobj <- BIFIEsurvey::BIFIE.data.transform( bifieobj3,
                transform.formula=transform.formula )
bifieobj$varnames[ bifieobj$varsindex.added ] <- c("pca_sc1", "pca_sc2","pca_sc3")
# check descriptive statistics
res9 <- BIFIEsurvey::BIFIE.univar( bifieobj, vars="pca_sc1", se=FALSE)
summary(res9)
res9$output$mean1M

# The transformation formula can also be conveniently generated by string operations
vars <- c("migrant", "female", "books", "lang" )
transform.formula2 <- as.formula( paste0( "~ 0 + I ( BIFIE.princomp( ~ ",
       paste0( vars, collapse="+" ),  ", 3 ) )") )
  ##   > transform.formula2
  ##   ~ I(BIFIE.princomp(~migrant + female + books + lang, 3))

#****************************
#*** Transformation 10: Overwriting variables books and migrant
bifieobj4 <-  BIFIEsurvey::BIFIE.data.transform( bifieobj3,
                  transform.formula=~ I( 1*(books >=1 ) ) + I(2*migrant),
                  varnames.new=c("books","migrant") )
summary(bifieobj4)

## End(Not run)

Statistical Inference for Derived Parameters

Description

This function performs statistical for derived parameters for objects of classes BIFIE.by, BIFIE.correl, BIFIE.crosstab, BIFIE.freq, BIFIE.linreg, BIFIE.logistreg and BIFIE.univar.

Usage

BIFIE.derivedParameters( BIFIE.method, derived.parameters, type=NULL)

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

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

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

Arguments

BIFIE.method

Object of classes BIFIE.by, BIFIE.correl, BIFIE.crosstab, BIFIE.freq, BIFIE.linreg, BIFIE.logistreg or BIFIE.univar (see parnames in the Output of these methods for saved parameters)

derived.parameters

List with R formulas for derived parameters (see Examples for specification)

type

Only applies to BIFIE.correl. In case of type="cov" covariances instead of correlations are used for derived parameters.

object

Object of class BIFIE.derivedParameters

digits

Number of digits for rounding decimals in output

...

Further arguments to be passed

Details

The distribution of derived parameters is derived by the direct calculation using original resampled parameters.

Value

A list with following entries

stat

Data frame with statistics

coef

Estimates of derived parameters

vcov

Covariance matrix of derived parameters

parnames

Parameter names

res_wald

Output of Wald test (global test regarding all parameters)

...

More values

See Also

See also BIFIE.waldtest for multi-parameter tests.

See car::deltaMethod for the Delta method assuming that the multivariate distribution of the parameters is asymptotically normal.

Examples

#############################################################################
# EXAMPLE 1: Imputed TIMSS dataset
#            Inference for correlations and derived parameters
#############################################################################

data(data.timss1)
data(data.timssrep)

# create BIFIE.dat object
bdat <- BIFIEsurvey::BIFIE.data( data.list=data.timss1, wgt=data.timss1[[1]]$TOTWGT,
           wgtrep=data.timssrep[, -1 ] )

# compute correlations
res1 <- BIFIEsurvey::BIFIE.correl( bdat,
            vars=c("ASSSCI", "ASMMAT", "books", "migrant" )  )
summary(res1)
res1$parnames
  ##    [1] "ASSSCI_ASSSCI"   "ASSSCI_ASMMAT"   "ASSSCI_books"    "ASSSCI_migrant"
  ##    [5] "ASMMAT_ASMMAT"   "ASMMAT_books"    "ASMMAT_migrant"  "books_books"
  ##    [9] "books_migrant"   "migrant_migrant"

# define four derived parameters
derived.parameters <- list(
        # squared correlation of science and mathematics
        "R2_sci_mat"=~ I( 100* ASSSCI_ASMMAT^2  ),
        # partial correlation of science and mathematics controlling for books
        "parcorr_sci_mat"=~ I( ( ASSSCI_ASMMAT - ASSSCI_books * ASMMAT_books ) /
                            sqrt(( 1 - ASSSCI_books^2 ) * ( 1-ASMMAT_books^2 ) ) ),
        # original correlation science and mathematics (already contained in res1)
        "cor_sci_mat"=~ I(ASSSCI_ASMMAT),
        # original correlation books and migrant
        "cor_book_migra"=~ I(books_migrant)
        )

# statistical inference for derived parameters
res2 <- BIFIEsurvey::BIFIE.derivedParameters( res1, derived.parameters )
summary(res2)

Empirical Distribution Function and Quantiles

Description

Computes an empirical distribution function (and quantiles). If only some quantiles should be calculated, then an appropriate vector of breaks (which are quantiles) must be specified. Statistical inference is not conducted for this method.

Usage

BIFIE.ecdf( BIFIEobj, vars, breaks=NULL, quanttype=1, group=NULL, group_values=NULL )

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

Arguments

BIFIEobj

Object of class BIFIEdata

vars

Vector of variables for which statistics should be computed.

breaks

Optional vector of breaks. Otherwise, it will be automatically defined.

quanttype

Type of calculation for quantiles. In case of quanttype=1, a linear interpolation is used (which is type='i/n' in Hmisc::wtd.quantile), while for quanttype=2 no interpolation is used.

group

Optional grouping variable

group_values

Optional vector of grouping values. This can be omitted and grouping values will be determined automatically.

object

Object of class BIFIE.ecdf

digits

Number of digits for rounding output

...

Further arguments to be passed

Value

A list with following entries

ecdf

Data frame with probabilities and the empirical distribution function (See Examples).

stat

Data frame with empirical distribution function stacked with respect to variables, groups and group values

output

More extensive output

...

More values

See Also

Hmisc::wtd.ecdf, Hmisc::wtd.quantile

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 ] )

# ecdf
vars <- c( "ASMMAT", "books")
group <- "female" ; group_values <- 0:1
# quantile type 1
res1 <- BIFIEsurvey::BIFIE.ecdf( bifieobj,  vars=vars, group=group )
summary(res1)
res2 <- BIFIEsurvey::BIFIE.ecdf( bifieobj,  vars=vars, group=group, quanttype=2)
# plot distribution function
ecdf1 <- res1$ecdf
plot( ecdf1$ASMMAT_female0, ecdf1$yval, type="l")
plot( res2$ecdf$ASMMAT_female0, ecdf1$yval, type="l", lty=2)
plot( ecdf1$books_female0, ecdf1$yval, type="l", col="blue")

Frequency Statistics

Description

Computes absolute and relative frequencies.

Usage

BIFIE.freq(BIFIEobj, vars, group=NULL, group_values=NULL, se=TRUE)

## S3 method for class 'BIFIE.freq'
summary(object,digits=3,...)

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

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

Arguments

BIFIEobj

Object of class BIFIEdata

vars

Vector of variables for which statistics should be computed

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.

object

Object of class BIFIE.freq

digits

Number of digits for rounding output

...

Further arguments to be passed

Value

A list with following entries

stat

Data frame with frequency statistics

output

Extensive output with all replicated statistics

...

More values

See Also

survey::svytable, intsvy::timss.table, Hmisc::wtd.table

Examples

#############################################################################
# EXAMPLE 1: Imputed TIMSS dataset
#############################################################################

data(data.timss1)
data(data.timssrep)

# create BIFIE.dat object
bdat <- BIFIEsurvey::BIFIE.data( data.list=data.timss1, wgt=data.timss1[[1]]$TOTWGT,
           wgtrep=data.timssrep[, -1 ] )

# Frequencies for three variables
res1 <- BIFIEsurvey::BIFIE.freq( bdat, vars=c("lang", "books", "migrant" )  )
summary(res1)

# Frequencies splitted by gender
res2 <- BIFIEsurvey::BIFIE.freq( bdat, vars=c("lang", "books", "migrant" ),
              group="female", group_values=0:1 )
summary(res2)

# Frequencies splitted by gender and likesc
res3 <- BIFIEsurvey::BIFIE.freq( bdat, vars=c("lang", "books", "migrant" ),
              group=c("likesc","female")  )
summary(res3)

Histogram

Description

Computes a histogram with same output as in graphics::hist. Statistical inference is not conducted for this method.

Usage

BIFIE.hist( BIFIEobj, vars, breaks=NULL, group=NULL, group_values=NULL  )

## S3 method for class 'BIFIE.hist'
summary(object,...)

## S3 method for class 'BIFIE.hist'
plot(x,ask=TRUE,...)

Arguments

BIFIEobj

Object of class BIFIEdata

vars

Vector of variables for which statistics should be computed.

breaks

Optional vector of breaks. Otherwise, it will be automatically defined.

group

Optional grouping variable(s)

group_values

Optional vector of grouping values. This can be omitted and grouping values will be determined automatically.

object

Object of class BIFIE.hist

x

Object of class BIFIE.hist

ask

Optional logical whether it should be asked for new plots.

...

Further arguments to be passed

Value

A list with following entries

histobj

List with objects of class histogram

output

More extensive output

...

More values

See Also

graphics::hist

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 ] )

# histogram
res1 <- BIFIEsurvey::BIFIE.hist( bifieobj, vars="ASMMAT", group="female" )
# plot histogram for first group (female=0)
plot( res1$histobj$ASMMAT_female0, col="lightblue")
# plot both histograms after each other
plot( res1 )

# user-defined vector of breaks
res2 <- BIFIEsurvey::BIFIE.hist( bifieobj, vars="ASMMAT",
              breaks=seq(0,900,10), group="female" )
plot( res2, col="orange")

Fitting a Model in lavaan or in survey

Description

The function BIFIE.lavaan.survey fits a structural equation model in lavaan using the lavaan.survey package (currently not on CRAN). Currently, only maximum likelihood estimation for normally distributed data is available.

The function BIFIE.survey fits a model defined in the survey package.

Usage

BIFIE.lavaan.survey(lavmodel, svyrepdes, lavaan_fun="sem",
    lavaan_survey_default=FALSE, fit.measures=NULL, ...)

## S3 method for class 'BIFIE.lavaan.survey'
summary(object, ...)

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

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

BIFIE.survey(svyrepdes, survey.function, ...)

## S3 method for class 'BIFIE.survey'
summary(object, digits=3, ...)

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

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

Arguments

lavmodel

Model string in lavaan syntax

svyrepdes

Replication design object of class BIFIEdata or replication design object from survey package (generated by BIFIEdata2svrepdesign or survey::svrepdesign)

lavaan_fun

Estimation funcion in lavaan. Can be "lavaan", "sem", "cfa" or "growth".

lavaan_survey_default

Logical indicating whether the lavaan.survey package should be used for statistical inference for multiply imputed datasets.

object

Object of class BIFIE.by

fit.measures

Optional vector of fit measures used in lavaan::fitMeasures function

...

Further arguments to be passed

survey.function

Function from the survey package

digits

Number of digits after decimal

Value

For BIFIE.lavaan.survey a list with following entries

lavfit

Object of class lavaan

fitstat

Fit statistics from lavaan

See Also

lavaan::lavaan, lavaan.survey::lavaan.survey

Examples

## Not run: 
#############################################################################
# EXAMPLE 1: Multiply imputed datasets, TIMSS replication design
#############################################################################

library(lavaan)
data(data.timss2)
data(data.timssrep)

#--- create BIFIEdata object
bdat4 <- BIFIEsurvey::BIFIE.data( data=data.timss2, wgt="TOTWGT",
                wgtrep=data.timssrep[,-1], fayfac=1)
print(bdat4)

#--- create survey object with conversion function
svydes4 <- BIFIEsurvey::BIFIEdata2svrepdesign(bdat4)

#*** regression model
mod1 <- BIFIEsurvey::BIFIE.linreg(bdat4, formula=ASMMAT ~ ASSSCI )
mod2 <- mitools::MIcombine( with(svydes4, survey::svyglm( formula=ASMMAT ~ ASSSCI,
                       design=svydes4 )))
#--- regression with lavaan.survey package
lavmodel <- "ASMMAT ~ 1
             ASMMAT ~ ASSSCI"
mod3 <- BIFIEsurvey::BIFIE.lavaan.survey(lavmodel, svyrepdes=svydes4)
# inference included in lavaan.survey package
mod4 <- BIFIEsurvey::BIFIE.lavaan.survey(lavmodel, svyrepdes=svydes4,
                        lavaan_survey_default=TRUE)
summary(mod3)
# extract fit statistics
lavaan::fitMeasures(mod3$lavfit)

#--- use BIFIE.lavaan.survey function with BIFIEdata object
mod5 <- BIFIEsurvey::BIFIE.lavaan.survey(lavmodel, svyrepdes=bdat4)
summary(mod5)

# compare estimated parameters
coef(mod1); coef(mod2); coef(mod3); coef(mod4); coef(mod5)

# compare standard error estimates
se(mod1); BIFIEsurvey::se(mod2); BIFIEsurvey::se(mod3); BIFIEsurvey::se(mod4); BIFIEsurvey::se(mod5)

#############################################################################
# EXAMPLE 2: Examples BIFIE.survey function
#############################################################################

data(data.timss2)
data(data.timssrep)

#--- create BIFIEdata object
bdat <- BIFIEsurvey::BIFIE.data( data=data.timss2, wgt="TOTWGT",
              wgtrep=data.timssrep[,-1], fayfac=1)
print(bdat)

#--- survey object
sdat <- BIFIEsurvey::BIFIEdata2svrepdesign(bdat)
print(sdat)

#- fit models in survey
mod1 <- BIFIEsurvey::BIFIE.linreg(bdat, formula=ASMMAT~ASSSCI)
mod2 <- BIFIEsurvey::BIFIE.survey( sdat, survey.function=survey::svyglm,
                                    formula=ASMMAT~ASSSCI)
mod3 <- BIFIEsurvey::BIFIE.survey( bdat, survey.function=survey::svyglm,
                                     formula=ASMMAT~ASSSCI)
summary(mod1)
summary(mod2)
summary(mod3)

#############################################################################
# EXAMPLE 3: Nested multiply imputed datasets | linear regression
#############################################################################

library(lavaan)
data(data.timss4)
data(data.timssrep)

# nested imputed dataset
bdat <- BIFIEsurvey::BIFIE.data( data.list=data.timss4,
            wgt=data.timss4[[1]][[1]]$TOTWGT, wgtrep=data.timssrep[, -1 ],  NMI=TRUE )
summary(bdat)

#*** BIFIEsurvey::BIFIE.linreg
mod1 <- BIFIEsurvey::BIFIE.linreg(bdat, formula=ASMMAT ~ migrant )

#*** survey::svyglm
mod2 <- BIFIEsurvey::BIFIE.survey(bdat, survey.function=survey::svyglm,
                                    formula=ASMMAT~migrant)

#*** lavaan.survey::lavaan.survey
lavmodel <- "ASMMAT ~ 1
             ASMMAT ~ migrant"
mod3 <- BIFIEsurvey::BIFIE.lavaan.survey(lavmodel, svyrepdes=bdat)

coef(mod1); coef(mod2); coef(mod3)
se(mod1); BIFIEsurvey::se(mod2), BIFIEsurvey::se(mod3)

## End(Not run)

Linear Regression

Description

Computes linear regression.

Usage

BIFIE.linreg(BIFIEobj, dep=NULL, pre=NULL, formula=NULL,
    group=NULL, group_values=NULL, se=TRUE)

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

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

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

Arguments

BIFIEobj

Object of class BIFIEdata

dep

String for the dependent variable in the regression model

pre

Vector of predictor variables. If the intercept should be included, then use the variable one for specifying it (see Examples).

formula

An R formula object which can be applied instead of providing dep and pre. Note that there is additional computation time needed for model matrix creation.

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.

object

Object of class BIFIE.linreg

digits

Number of digits for rounding output

...

Further arguments to be passed

Value

A list with following entries

stat

Data frame with unstandardized and standardized regression coefficients, residual standard deviation and R2R^2

output

Extensive output with all replicated statistics

...

More values

See Also

Alternative implementations: survey::svyglm, intsvy::timss.reg, intsvy::timss.reg.pv, stats::lm

See BIFIE.logistreg for logistic regression.

Examples

#############################################################################
# EXAMPLE 1: Imputed TIMSS dataset
#############################################################################

data(data.timss1)
data(data.timssrep)

# create BIFIE.dat object
bdat <- BIFIEsurvey::BIFIE.data( data.list=data.timss1, wgt=data.timss1[[1]]$TOTWGT,
             wgtrep=data.timssrep[, -1 ] )

#**** Model 1: Linear regression for mathematics score
mod1 <- BIFIEsurvey::BIFIE.linreg( bdat, dep="ASMMAT", pre=c("one","books","migrant"),
              group="female" )
summary(mod1)

## Not run: 
# same model but specified with R formulas
mod1a <- BIFIEsurvey::BIFIE.linreg( bdat, formula=ASMMAT ~ books + migrant,
               group="female", group_values=0:1 )
summary(mod1a)

# compare result with lm function and first imputed dataset
dat1 <- data.timss1[[1]]
mod1b <- stats::lm( ASMMAT ~ 0 + as.factor(female) + as.factor(female):books +
                              as.factor(female):migrant,
                         data=dat1,  weights=dat1$TOTWGT )
summary(mod1b)

#**** Model 2: Like Model 1, but books is now treated as a factor
mod2 <- BIFIEsurvey::BIFIE.linreg( bdat, formula=ASMMAT ~ as.factor(books) + migrant)
summary(mod2)

#############################################################################
# EXAMPLE 2: PISA data | Nonlinear regression models
#############################################################################

data(data.pisaNLD)
data <- data.pisaNLD

#--- Create BIFIEdata object immediately using BIFIE.data.jack function
bdat <- BIFIEsurvey::BIFIE.data.jack( data.pisaNLD, jktype="RW_PISA", cdata=TRUE)
summary(bdat)

#****************************************************
#*** Model 1: linear regression
mod1 <- BIFIEsurvey::BIFIE.linreg( bdat, formula=MATH ~ HISEI )
summary(mod1)

#****************************************************
#*** Model 2: Cubic regression
mod2 <- BIFIEsurvey::BIFIE.linreg( bdat, formula=MATH ~ HISEI + I(HISEI^2) + I(HISEI^3) )
summary(mod2)

#****************************************************
#*** Model 3: B-spline regression

# test with design of HISEI values
dfr <- data.frame("HISEI"=16:90 )
des <- stats::model.frame( ~ splines::bs( HISEI, df=5 ), dfr )
des <- des$splines
plot( dfr$HISEI, des[,1], type="l", pch=1, lwd=2, ylim=c(0,1) )
for (vv in 2:ncol(des) ){
    lines( dfr$HISEI, des[,vv], lty=vv, col=vv, lwd=2)
}

# apply B-spline regression in BIFIEsurvey::BIFIE.linreg
mod3 <- BIFIEsurvey::BIFIE.linreg( bdat, formula=MATH ~ splines::bs(HISEI,df=5) )
summary(mod3)

#*** include transformed HISEI values for B-spline matrix in bdat
bdat2 <- BIFIEsurvey::BIFIE.data.transform( bdat, ~ 0 + splines::bs( HISEI, df=5 ))
bdat2$varnames[ bdat2$varsindex.added ] <- paste0("HISEI_bsdes",
            seq( 1, length( bdat2$varsindex.added ) ) )

#****************************************************
#*** Model 4: Nonparametric regression using BIFIE.by

?BIFIE.by

#---- (1) test function with one dataset
dat1 <- bdat$dat1
vars <- c("MATH", "HISEI")
X <- dat1[,vars]
w <- bdat$wgt
X <- as.data.frame(X)
# estimate model
mod <- stats::loess( MATH ~ HISEI, weights=w, data=X )

# predict HISEI values
hisei_val <- data.frame( "HISEI"=seq(16,90) )
y_pred <- stats::predict( mod, hisei_val )
graphics::plot( hisei_val$HISEI, y_pred, type="l")

#--- (2) define loess function
loess_fct <- function(X,w){
    X1 <- data.frame( X, w )
    colnames(X1) <- c( vars, "wgt")
    X1 <- stats::na.omit(X1)
#    mod <- stats::lm( MATH ~ HISEI, weights=X1$wgt, data=X1 )
    mod <- stats::loess( MATH ~ HISEI, weights=X1$wgt, data=X1 )
    y_pred <- stats::predict( mod, hisei_val )
    return(y_pred)
}

#--- (3) estimate model
mod4 <-  BIFIEsurvey::BIFIE.by( bdat, vars, userfct=loess_fct )
summary(mod4)

# plot linear function pointwise and confidence intervals
graphics::plot( hisei_val$HISEI, mod4$stat$est, type="l", lwd=2,
        xlab="HISEI", ylab="PVMATH", ylim=c(430,670) )
graphics::lines( hisei_val$HISEI, mod4$stat$est - 1.96* mod4$stat$SE, lty=3 )
graphics::lines( hisei_val$HISEI, mod4$stat$est + 1.96* mod4$stat$SE, lty=3 )

## End(Not run)

Logistic Regression

Description

Computes logistic regression. Explained variance R2R^2 is computed by the approach of McKelvey and Zavoina.

Usage

BIFIE.logistreg(BIFIEobj, dep=NULL, pre=NULL, formula=NULL,
    group=NULL, group_values=NULL, se=TRUE, eps=1E-8, maxiter=100)

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

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

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

Arguments

BIFIEobj

Object of class BIFIEdata

dep

String for the dependent variable in the regression model

pre

Vector of predictor variables. If the intercept should be included, then use the variable one for specifying it (see Examples).

formula

An R formula object which can be applied instead of providing dep and pre. Note that there is additional computation time needed for model matrix creation.

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.

eps

Convergence criterion for parameters

maxiter

Maximum number of iterations

object

Object of class BIFIE.logistreg

digits

Number of digits for rounding output

...

Further arguments to be passed

Value

A list with following entries

stat

Data frame with regression coefficients

output

Extensive output with all replicated statistics

...

More values

See Also

survey::svyglm, stats::glm

For linear regressions see BIFIE.linreg.

Examples

#############################################################################
# EXAMPLE 1: TIMSS dataset | Logistic regression
#############################################################################

data(data.timss2)
data(data.timssrep)

# create BIFIE.dat object
bdat <- BIFIEsurvey::BIFIE.data( data.list=data.timss2, wgt=data.timss2[[1]]$TOTWGT,
                      wgtrep=data.timssrep[, -1 ] )

#**** Model 1: Logistic regression - prediction of migrational background
res1 <- BIFIEsurvey::BIFIE.logistreg( BIFIEobj=bdat, dep="migrant",
           pre=c("one","books","lang"), group="female", se=FALSE )
summary(res1)

## Not run: 
# same model, but with formula specification and standard errors
res1a <- BIFIEsurvey::BIFIE.logistreg( BIFIEobj=bdat,
              formula=migrant ~ books + lang, group="female"  )
summary(res1a)

#############################################################################
# SIMULATED EXAMPLE 2: Comparison of stats::glm and BIFIEsurvey::BIFIE.logistreg
#############################################################################

#*** (1) simulate data
set.seed(987)
N <- 300
x1 <- stats::rnorm(N)
x2 <- stats::runif(N)
ypred <- -0.75+.2*x1 + 3*x2
y <- 1*( stats::plogis(ypred) > stats::runif(N) )
data <- data.frame( "y"=y, "x1"=x1, "x2"=x2 )

#*** (2) estimation logistic regression using glm
mod1 <- stats::glm( y ~ x1 + x2, family="binomial")

#*** (3) estimation logistic regression using BIFIEdata
# create BIFIEdata object by defining 30 Jackknife zones
bifiedata <- BIFIEsurvey::BIFIE.data.jack( data, jktype="JK_RANDOM", ngr=30 )
summary(bifiedata)
# estimate logistic regression
mod2 <- BIFIEsurvey::BIFIE.logistreg( bifiedata, formula=y ~ x1+x2 )

#*** (4) compare results
summary(mod2)    # BIFIE.logistreg
summary(mod1)   # glm

## End(Not run)

Missing Value Analysis

Description

Conducts a missing value analysis.

Usage

BIFIE.mva( BIFIEobj, missvars, covariates=NULL, se=TRUE )

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

Arguments

BIFIEobj

Object of class BIFIEdata

missvars

Vector of variables for which missing value statistics should be computed

covariates

Vector of variables which work as covariates

se

Optional logical indicating whether statistical inference based on replication should be employed.

object

Object of class BIFIE.correl

digits

Number of digits for rounding output

...

Further arguments to be passed

Value

A list with following entries

stat.mva

Data frame with missing value statistics

res_list

List with extensive output split according to each variable in missvars

...

More values

Examples

#############################################################################
# EXAMPLE 1: Imputed TIMSS dataset
#############################################################################

data(data.timss1)
data(data.timssrep)

# create BIFIE.dat object
BIFIEdata <- BIFIEsurvey::BIFIE.data( data.list=data.timss1,
                wgt=data.timss1[[1]]$TOTWGT, wgtrep=data.timssrep[, -1 ] )

# missing value analysis for "scsci" and "books" and three covariates
res1 <- BIFIEsurvey::BIFIE.mva( BIFIEdata, missvars=c("scsci", "books" ),
             covariates=c("ASMMAT", "female", "ASSSCI") )
summary(res1)

# missing value analysis without statistical inference and without covariates
res2 <- BIFIEsurvey::BIFIE.mva( BIFIEdata, missvars=c("scsci", "books"), se=FALSE)
summary(res2)

Path Model Estimation

Description

This function computes a path model. Predictors are allowed to possess measurement errors. Known measurement error variances (and covariances) or reliabilities can be specified by the user. Alternatively, a set of indicators can be defined for each latent variable, and for each imputed and replicated dataset the measurement error variance is determined by means of calculating the reliability Cronbachs alpha. Measurement errors are handled by adjusting covariance matrices (see Buonaccorsi, 2010, Ch. 5).

Usage

BIFIE.pathmodel( BIFIEobj, lavaan.model, reliability=NULL, group=NULL,
        group_values=NULL, se=TRUE )

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

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

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

Arguments

BIFIEobj

Object of class BIFIEdata

lavaan.model

String including the model specification in lavaan syntax. lavaan.model also allows the extended functionality in the TAM::lavaanify.IRT function.

reliability

Optional vector containing the reliabilities of each variable. This vector can also include only a subset of all variables.

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.

object

Object of class BIFIE.pathmodel

digits

Number of digits for rounding output

...

Further arguments to be passed

Details

The following conventions are used as parameter labels in the output.

Y~X is the regression coefficient of the regression from YY on XX.

X->Z->Y denotes the path coefficient from XX to YY passing the mediating variable ZZ.

X-+>Y denotes the total effect (of all paths) from XX to YY.

X-~>Y denotes the sum of all indirect effects from XX to YY.

The parameter suffix _stand refers to parameters for which all variables are standardized.

Value

A list with following entries

stat

Data frame with unstandardized and standardized regression coefficients, path coefficients, total and indirect effects, residual variances, and R2R^2

output

Extensive output with all replicated statistics

...

More values

References

Buonaccorsi, J. P. (2010). Measurement error: Models, methods, and applications. CRC Press.

See Also

See the lavaan and lavaan.survey package.

For the lavaan syntax, see lavaan::lavaanify and TAM::lavaanify.IRT

Examples

## Not run: 
#############################################################################
# EXAMPLE 1: Path model data.bifie01
#############################################################################

data(data.bifie01)
dat <- data.bifie01
# create dataset with replicate weights and plausible values
bifieobj <- BIFIEsurvey::BIFIE.data.jack( data=dat,  jktype="JK_TIMSS",
                jkzone="JKCZONE", jkrep="JKCREP", wgt="TOTWGT",
                pv_vars=c("ASMMAT","ASSSCI") )

#**************************************************************
#*** Model 1: Path model
lavmodel1 <- "
     ASMMAT ~ ASBG07A + ASBG07B  + ASBM03 + ASBM02A + ASBM02E
     # define latent variable with 2nd and 3rd item in reversed scoring
     ASBM03=~ 1*ASBM03A + (-1)*ASBM03B + (-1)*ASBM03C + 1*ASBM03D
     ASBG07A ~ ASBM02E
     ASBG07A ~~ .2*ASBG07A    # measurement error variance of .20
     ASBM02E ~~ .45*ASBM02E     # measurement error variance of .45
     ASBM02E ~ ASBM02A + ASBM02B
        "
#--- Model 1a: model calculated by gender
mod1a <- BIFIEsurvey::BIFIE.pathmodel( bifieobj, lavmodel1, group="female" )
summary(mod1a)

#--- Model 1b: Input of some known reliabilities
reliability <- c( "ASBM02B"=.6, "ASBM02A"=.8 )
mod1b <- BIFIEsurvey::BIFIE.pathmodel( bifieobj, lavmodel1, reliability=reliability)
summary(mod1b)

#**************************************************************
#*** Model 2: Linear regression with errors in predictors

# specify lavaan model
lavmodel2 <- "
     ASMMAT ~ ASBG07A + ASBG07B + ASBM03A
     ASBG07A ~~ .2*ASBG07A
        "
mod2 <- BIFIEsurvey::BIFIE.pathmodel( bifieobj, lavmodel2  )
summary(mod2)

## End(Not run)

Two Level Regression

Description

This function computes the hierarchical two level model with random intercepts and random slopes. The full maximum likelihood estimation is conducted by means of an EM algorithm (Raudenbush & Bryk, 2002).

Usage

BIFIE.twolevelreg( BIFIEobj, dep, formula.fixed, formula.random, idcluster,
   wgtlevel2=NULL, wgtlevel1=NULL, group=NULL, group_values=NULL,
   recov_constraint=NULL, se=TRUE, globconv=1E-6, maxiter=1000 )

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

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

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

Arguments

BIFIEobj

Object of class BIFIEdata

dep

String for the dependent variable in the regression model

formula.fixed

An R formula for fixed effects

formula.random

An R formula for random effects

idcluster

Cluster identifier. The cluster identifiers must be sorted in the BIFIE.data object.

wgtlevel2

Name of Level 2 weight variable

wgtlevel1

Name of Level 1 weight variable. This is optional. If it is not provided, wgtlevel is calculated from the total weight and wgtlevel2.

group

Optional grouping variable

group_values

Optional vector of grouping values. This can be omitted and grouping values will be determined automatically.

recov_constraint

Matrix for constraints of random effects covariance matrix. The random effects are numbered according to the order in the specification in formula.random. The first column in recov_constraint contains the row index in the covariance matrix, the second column the column index and the third column the value to be fixed.

se

Optional logical indicating whether statistical inference based on replication should be employed. In case of se=FALSE, standard errors are computed as maximum likelihood estimates under the assumption of random sampling of level 2 clusters.

globconv

Convergence criterion for maximum parameter change

maxiter

Maximum number of iterations

object

Object of class BIFIE.twolevelreg

digits

Number of digits for rounding output

...

Further arguments to be passed

Details

The implemented random slope model can be written as

yij=Xijγ+Zijuj+εijy_{ij}=\bold{X}_{ij} \bold{\gamma} + \bold{Z}_{ij} \bold{u}_j + \varepsilon_{ij}

where yijy_{ij} is the dependent variable, Xij\bold{X}_{ij} includes the fixed effects predictors (specified by formula.fixed) and Zij\bold{Z}_{ij} includes the random effects predictors (specified by formula.random). The random effects uj\bold{u}_j follow a multivariate normal distribution.

The function also computes a variance decomposition of explained variance due to fixed and random effects for the within and the between level. This variance decomposition is conducted for the predictor matrices X\bold{X} and Z\bold{Z}. It is assumed that Xij=XjB+XijW\bold{X}_{ij}=\bold{X}_j^B + \bold{X}_{ij}^W. The different sources of variance are computed by formulas as proposed in Snijders and Bosker (2012, Ch. 7).

Value

A list with following entries

stat

Data frame with coefficients and different sources of variance.

output

Extensive output with all replicated statistics

...

More values

References

Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models: Applications and data analysis methods. Thousand Oaks: Sage.

Snijders, T. A. B., & Bosker, R. J. (2012). Multilevel analysis: An introduction to basic and advanced multilevel modeling. Thousand Oaks: Sage.

See Also

The lme4::lmer function in the lme4 package allows only weights at the first level.

See the WeMix package (and the function WeMix::mix) for estimation of mixed effects models with weights at different levels.

Examples

## Not run: 
library(lme4)

#############################################################################
# EXAMPLE 1: Dataset data.bifie01 | TIMSS 2011
#############################################################################

data(data.bifie01)
dat <- data.bifie01
set.seed(987)

# create dataset with replicate weights and plausible values
bdat1 <- BIFIEsurvey::BIFIE.data.jack( data=dat, jktype="JK_TIMSS", jkzone="JKCZONE",
            jkrep="JKCREP", wgt="TOTWGT", pv_vars=c("ASMMAT","ASSSCI") )

# create dataset without plausible values and ignoring weights
bdat2 <- BIFIEsurvey::BIFIE.data.jack( data=dat, jktype="JK_RANDOM", ngr=10 )
#=> standard errors from ML estimation

#***********************************************
# Model 1: Random intercept model

#--- Model 1a: without weights, first plausible value
mod1a <- BIFIEsurvey::BIFIE.twolevelreg( BIFIEobj=bdat2, dep="ASMMAT01",
                formula.fixed=~ 1, formula.random=~ 1, idcluster="idschool",
                wgtlevel2="one", se=FALSE )
summary(mod1a)

#--- Model 1b: estimation in lme4
mod1b <- lme4::lmer( ASMMAT01 ~ 1 + ( 1 | idschool), data=dat, REML=FALSE)
summary(mod1b)

#--- Model 1c: Like Model 1a but for five plausible values and ML inference
mod1c <- BIFIEsurvey::BIFIE.twolevelreg( BIFIEobj=bdat1, dep="ASMMAT",
                formula.fixed=~ 1, formula.random=~ 1, idcluster="idschool",
                wgtlevel2="one",  se=FALSE )
summary(mod1c)

#--- Model 1d: weights and sampling design and all plausible values
mod1d <- BIFIEsurvey::BIFIE.twolevelreg( BIFIEobj=bdat1, dep="ASMMAT",
                formula.fixed=~ 1, formula.random=~ 1, idcluster="idschool",
                wgtlevel2="SCHWGT" )
summary(mod1d)

#***********************************************
# Model 2: Random slope model

#--- Model 2a: without weights
mod2a <- BIFIEsurvey::BIFIE.twolevelreg( BIFIEobj=bdat2, dep="ASMMAT01",
                formula.fixed=~  female +  ASBG06A, formula.random=~ ASBG06A,
                idcluster="idschool", wgtlevel2="one",  se=FALSE )
summary(mod2a)

#--- Model 2b: estimation in lme4
mod2b <- lme4::lmer( ASMMAT01 ~ female +  ASBG06A + ( 1 + ASBG06A | idschool),
                   data=dat, REML=FALSE)
summary(mod2b)

#--- Model 2c: weights and sampling design and all plausible values
mod2c <- BIFIEsurvey::BIFIE.twolevelreg( BIFIEobj=bdat1, dep="ASMMAT",
                formula.fixed=~  female +  ASBG06A, formula.random=~ ASBG06A,
                idcluster="idschool", wgtlevel2="SCHWGT", maxiter=500, se=FALSE)
summary(mod2c)

#--- Model 2d: Uncorrelated intecepts and slopes

# constraint for zero covariance between intercept and slope
recov_constraint <- matrix( c(1,2,0), ncol=3 )
mod2d <- BIFIEsurvey::BIFIE.twolevelreg( BIFIEobj=bdat2, dep="ASMMAT01",
                formula.fixed=~ female +  ASBG06A, formula.random=~ ASBG06A,
                idcluster="idschool", wgtlevel2="one",  se=FALSE,
                recov_constraint=recov_constraint )
summary(mod2d)

#--- Model 2e: Fixed entries in the random effects covariance matrix

# two constraints for random effects covariance
# Cov(Int, Slo)=0  # zero slope for intercept and slope
# Var(Slo)=10      # slope variance of 10
recov_constraint <- matrix( c(1,2,0,
                      2,2,10), ncol=3, byrow=TRUE)
mod2e <- BIFIEsurvey::BIFIE.twolevelreg( BIFIEobj=bdat2, dep="ASMMAT01",
                formula.fixed=~  female +  ASBG06A, formula.random=~ ASBG06A,
                idcluster="idschool", wgtlevel2="one",  se=FALSE,
                recov_constraint=recov_constraint )
summary(mod2e)

#############################################################################
# SIMULATED EXAMPLE 2: Two-level regression with random slopes
#############################################################################

#--- (1) simulate data
set.seed(9876)
NC <- 100    # number of clusters
Nj <- 20     # number of persons per cluster
iccx <- .4   # intra-class correlation predictor
theta <- c( 0.7, .3 )    # fixed effects
Tmat <- diag( c(.3, .1 ) ) # variances of random intercept and slope
sig2 <- .60    # residual variance
N <- NC*Nj
idcluster <- rep( 1:NC, each=Nj )
dat1 <- data.frame("idcluster"=idcluster )
dat1$X <- rep( stats::rnorm( NC, sd=sqrt(iccx) ), each=Nj ) +
                 stats::rnorm( N, sd=sqrt( 1 - iccx) )
dat1$Y <- theta[1] + rep( stats::rnorm(NC, sd=sqrt(Tmat[1,1] ) ), each=Nj ) +
      theta[2] + rep( stats::rnorm(NC, sd=sqrt(Tmat[2,2])), each=Nj )) * dat1$X +
      stats::rnorm(N, sd=sqrt(sig2) )

#--- (2) create design object
bdat1 <- BIFIEsurvey::BIFIE.data.jack( data=dat1, jktype="JK_GROUP", jkzone="idcluster")
summary(bdat1)

#*** Model 1: Random slope model (ML standard errors)

#- estimation using BIFIE.twolevelreg
mod1a <- BIFIEsurvey::BIFIE.twolevelreg( BIFIEobj=bdat1, dep="Y",
                formula.fixed=~ 1+X, formula.random=~ 1+X, idcluster="idcluster",
                wgtlevel2="one",  se=FALSE )
summary(mod1a)

#- estimation in lme4
mod1b <- lme4::lmer( Y ~ X + ( 1+X | idcluster), data=dat1, REML=FALSE  )
summary(mod1b)

#- using Jackknife for inference
mod1c <- BIFIEsurvey::BIFIE.twolevelreg( BIFIEobj=bdat1, dep="Y",
                formula.fixed=~ 1+X, formula.random=~ 1+X, idcluster="idcluster",
                wgtlevel2="one",  se=TRUE )
summary(mod1c)

# extract coefficients
coef(mod1a)
coef(mod1c)
# covariance matrix
vcov(mod1a)
vcov(mod1c)

## End(Not run)

Univariate Descriptive Statistics (Means and Standard Deviations)

Description

Computes some univariate descriptive statistics (means and standard deviations).

Usage

BIFIE.univar(BIFIEobj, vars, group=NULL, group_values=NULL, se=TRUE)

## S3 method for class 'BIFIE.univar'
summary(object,digits=3,...)

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

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

Arguments

BIFIEobj

Object of class BIFIEdata

vars

Vector of variables for which statistics should be computed

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.

object

Object of class BIFIE.univar

digits

Number of digits for rounding output

...

Further arguments to be passed

Value

A list with following entries

stat

Data frame with univariate statistics

stat_M

Data frame with means

stat_SD

Data frame with standard deviations

output

Extensive output with all replicated statistics

...

More values

See Also

See BIFIE.univar.test for a test of equal means and effect sizes η\eta and dd.

Descriptive statistics without statistical inference can be estimated by the collection of miceadds::ma.wtd.statNA functions from the miceadds package.

Further descriptive functions:

survey::svymean, intsvy::timss.mean, intsvy::timss.mean.pv, stats::weighted.mean, Hmisc::wtd.mean, miceadds::ma.wtd.meanNA

survey::svyvar, Hmisc::wtd.var, miceadds::ma.wtd.sdNA, miceadds::ma.wtd.covNA

Examples

#############################################################################
# EXAMPLE 1: Imputed TIMSS dataset
#############################################################################

data(data.timss1)
data(data.timssrep)

# create BIFIE.dat object
bdat <- BIFIEsurvey::BIFIE.data( data.list=data.timss1, wgt=data.timss1[[1]]$TOTWGT,
           wgtrep=data.timssrep[, -1 ] )

# compute descriptives for plausible values
res1 <- BIFIEsurvey::BIFIE.univar( bdat, vars=c("ASMMAT","ASSSCI","books") )
summary(res1)

# split descriptives by number of books
res2 <- BIFIEsurvey::BIFIE.univar( bdat, vars=c("ASMMAT","ASSSCI"), group="books",
            group_values=1:5)
summary(res2)

#############################################################################
# EXAMPLE 2: TIMSS dataset with missings
#############################################################################

data(data.timss2)
data(data.timssrep)

# use first dataset with missing data from data.timss2
bdat1 <- BIFIEsurvey::BIFIE.data( data.list=data.timss2[[1]], wgt=data.timss2[[1]]$TOTWGT,
               wgtrep=data.timssrep[, -1 ])

# some descriptive statistics without statistical inference
res1a <- BIFIEsurvey::BIFIE.univar( bdat1, vars=c("ASMMAT","ASSSCI","books"), se=FALSE)
# descriptive statistics with statistical inference
res1b <- BIFIEsurvey::BIFIE.univar( bdat1, vars=c("ASMMAT","ASSSCI","books") )
summary(res1a)
summary(res1b)

# split descriptives by number of books
res2 <- BIFIEsurvey::BIFIE.univar( bdat1, vars=c("ASMMAT","ASSSCI"), group="books")
# Note that if group_values is not specified as an argument it will be
# automatically determined by the observed frequencies in the dataset
summary(res2)

Analysis of Variance and Effect Sizes for Univariate Statistics

Description

Computes a Wald test which tests equality of means (univariate analysis of variance). In addition, the dd and η\eta effect sizes are computed.

Usage

BIFIE.univar.test(BIFIE.method, wald_test=TRUE)

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

Arguments

BIFIE.method

Object of class BIFIE.univar

wald_test

Optional logical indicating whether a Wald test should be performed.

object

Object of class BIFIE.univar.test

digits

Number of digits for rounding output

...

Further arguments to be passed

Value

A list with following entries

stat.F

Data frame with FF statistic for Wald test

stat.eta

Data frame with η\eta effect size and its inference

stat.dstat

Data frame with Cohen's dd effect size and its inference

...

More values

See Also

BIFIE.univar

Examples

#############################################################################
# EXAMPLE 1: Imputed TIMSS dataset - One grouping variable
#############################################################################

data(data.timss1)
data(data.timssrep)

# create BIFIE.dat object
bdat <- BIFIEsurvey::BIFIE.data( data.list=data.timss1, wgt=data.timss1[[1]]$TOTWGT,
           wgtrep=data.timssrep[, -1 ] )

#**** Model 1: 3 variables splitted by book
res1 <- BIFIEsurvey::BIFIE.univar( bdat, vars=c("ASMMAT", "ASSSCI","scsci"),
                    group="books")
summary(res1)
# analysis of variance
tres1 <- BIFIEsurvey::BIFIE.univar.test(res1)
summary(tres1)

#**** Model 2: One variable splitted by gender
res2 <- BIFIEsurvey::BIFIE.univar( bdat, vars=c("ASMMAT"), group="female" )
summary(res2)
# analysis of variance
tres2 <- BIFIEsurvey::BIFIE.univar.test(res2)
summary(tres2)

## Not run: 
#**** Model 3: Univariate statistic: math
res3 <- BIFIEsurvey::BIFIE.univar( bdat, vars=c("ASMMAT") )
summary(res3)
tres3 <- BIFIEsurvey::BIFIE.univar.test(res3)

#############################################################################
# EXAMPLE 2: Imputed TIMSS dataset - Two grouping variables
#############################################################################

data(data.timss1)
data(data.timssrep)

# create BIFIE.dat object
bdat <- BIFIEsurvey::BIFIE.data( data.list=data.timss1, wgt=data.timss1[[1]]$TOTWGT,
                  wgtrep=data.timssrep[, -1 ] )

#**** Model 1: 3 variables splitted by book and female
res1 <- BIFIEsurvey::BIFIE.univar(bdat, vars=c("ASMMAT", "ASSSCI","scsci"),
                  group=c("books","female"))
summary(res1)

# analysis of variance
tres1 <- BIFIEsurvey::BIFIE.univar.test(res1)
summary(tres1)

# extract data frame with Cohens d statistic
dstat <- tres1$stat.dstat

# extract d values for gender comparisons with same value of books
# -> 'books' refers to the first variable
ind <- which(
  unlist( lapply( strsplit( dstat$groupval1, "#"), FUN=function(vv){vv[1]}) )==
  unlist( lapply( strsplit( dstat$groupval2, "#"), FUN=function(vv){vv[1]}) )
        )
dstat[ ind, ]

## End(Not run)

Wald Tests for BIFIE Methods

Description

This function performs a Wald test for objects of classes BIFIE.by, BIFIE.correl, BIFIE.crosstab, BIFIE.freq, BIFIE.linreg, BIFIE.logistreg and BIFIE.univar.

Usage

BIFIE.waldtest(BIFIE.method, Cdes, rdes, type=NULL)

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

Arguments

BIFIE.method

Object of classes BIFIE.by, BIFIE.correl, BIFIE.crosstab, BIFIE.freq, BIFIE.linreg, BIFIE.logistreg or BIFIE.univar (see parnames in the Output of these methods for saved parameters)

Cdes

Design matrix CC (see Details)

rdes

Design vector rr (see Details)

type

Only applies to BIFIE.correl. In case of type="cov" covariances instead of correlations are used for parameter tests.

object

Object of class BIFIE.waldtest

digits

Number of digits for rounding output

...

Further arguments to be passed

Details

The Wald test is conducted for a parameter vector θ\bold{\theta}, specifying the hypothesis Cθ=rC \bold{\theta}=r. Statistical inference is performed by using the D1D_1 and the D2D_2 statistic (Enders, 2010, Ch. 8).

For objects of class bifie.univar, only hypotheses with respect to means are implemented.

Value

A list with following entries

stat.D

Data frame with D1D_1 and D2D_2 statistic, degrees of freedom and p value

...

More values

References

Enders, C. K. (2010). Applied missing data analysis. Guilford Press.

See Also

survey::regTermTest, survey::anova.svyglm, car::linearHypothesis

Examples

#############################################################################
# EXAMPLE 1: Imputed TIMSS dataset
#############################################################################

data(data.timss1)
data(data.timssrep)

# create BIFIE.dat object
bdat <- BIFIEsurvey::BIFIE.data( data.list=data.timss1, wgt=data.timss1[[1]]$TOTWGT,
           wgtrep=data.timssrep[, -1 ] )

#******************
#*** Model 1: Linear regression
res1 <- BIFIEsurvey::BIFIE.linreg( bdat, dep="ASMMAT", pre=c("one","books","migrant"),
         group="female" )
summary(res1)

#*** Wald test which tests whether sigma and R^2 values are the same
res1$parnames    # parameter names
pn <- res1$parnames ; PN <- length(pn)
Cdes <- matrix(0,nrow=2, ncol=PN)
colnames(Cdes) <- pn
# equality of R^2  ( R^2(female0) - R^2(female1)=0 )
Cdes[ 1, c("R^2_NA_female_0", "R^2_NA_female_1" ) ] <- c(1,-1)
# equality of sigma ( sigma(female0) - sigma(female1)=0)
Cdes[ 2, c("sigma_NA_female_0", "sigma_NA_female_1" ) ] <- c(1,-1)
# design vector
rdes <- rep(0,2)
# perform Wald test
wmod1 <- BIFIEsurvey::BIFIE.waldtest( BIFIE.method=res1, Cdes=Cdes, rdes=rdes )
summary(wmod1)

## Not run: 
#******************
#*** Model 2: Correlations

# compute some correlations
res2a <- BIFIEsurvey::BIFIE.correl( bdat, vars=c("ASMMAT","ASSSCI","migrant","books"))
summary(res2a)

# test whether r(MAT,migr)=r(SCI,migr) and r(MAT,books)=r(SCI,books)
pn <- res2a$parnames; PN <- length(pn)
Cdes <- matrix( 0, nrow=2, ncol=PN )
colnames(Cdes) <- pn
Cdes[ 1, c("ASMMAT_migrant", "ASSSCI_migrant") ] <- c(1,-1)
Cdes[ 2, c("ASMMAT_books", "ASSSCI_books") ] <- c(1,-1)
rdes <- rep(0,2)
# perform Wald test
wres2a <- BIFIEsurvey::BIFIE.waldtest( res2a, Cdes, rdes )
summary(wres2a)

#******************
#*** Model 3: Frequencies

# Number of books splitted by gender
res3a <- BIFIEsurvey::BIFIE.freq( bdat, vars=c("books"), group="female" )
summary(res3a)

# test whether book(cat4,female0)+book(cat5,female0)=book(cat4,female1)+book(cat5,female5)
pn <- res3a$parnames
PN <- length(pn)
Cdes <- matrix( 0, nrow=1, ncol=PN )
colnames(Cdes) <- pn
Cdes[ 1, c("books_4_female_0", "books_5_female_0",
    "books_4_female_1", "books_5_female_1" ) ] <- c(1,1,-1,-1)
rdes <- c(0)
# Wald test
wres3a <- BIFIEsurvey::BIFIE.waldtest( res3a, Cdes, rdes )
summary(wres3a)

#******************
#*** Model 4: Means

# math and science score splitted by gender
res4a <- BIFIEsurvey::BIFIE.univar( bdat, vars=c("ASMMAT","ASSSCI"), group="female")
summary(res4a)

# test whether there are significant gender differences in math and science
#=> multivariate ANOVA
pn <- res4a$parnames
PN <- length(pn)
Cdes <- matrix( 0, nrow=2, ncol=PN )
colnames(Cdes) <- pn
Cdes[ 1, c("ASMMAT_female_0", "ASMMAT_female_1"  ) ] <- c(1,-1)
Cdes[ 2, c("ASSSCI_female_0", "ASSSCI_female_1"  ) ] <- c(1,-1)
rdes <- rep(0,2)
# Wald test
wres4a <- BIFIEsurvey::BIFIE.waldtest( res4a, Cdes, rdes )
summary(wres4a)

## End(Not run)

Selection of Variables and Imputed Datasets for Objects of Class BIFIEdata

Description

This function select variables and some (or all) imputed datasets of an object of class BIFIEdata and saves the resulting object also of class BIFIEdata.

Usage

BIFIEdata.select(bifieobj, varnames=NULL, impdata.index=NULL)

Arguments

bifieobj

Object of class BIFIEdata

varnames

Variables chosen for the selection

impdata.index

Selected indices of imputed datasets

Value

An object of class BIFIEdata saved in a non-compact or compact way, see value cdata

See Also

See BIFIE.data for creating BIFIEdata objects.

Examples

#############################################################################
# EXAMPLE 1: Some manipulations of BIFIEdata objects created from data.timss1
#############################################################################
data(data.timss1)
data(data.timssrep)

# create BIFIEdata
bdat1 <- BIFIEsurvey::BIFIE.data( data.list=data.timss1, wgt=data.timss1[[1]]$TOTWGT,
            wgtrep=data.timssrep[, -1 ])
summary(bdat1)

# create BIFIEcdata object
bdat2 <- BIFIEsurvey::BIFIE.data( data.list=data.timss1, wgt=data.timss1[[1]]$TOTWGT,
            wgtrep=data.timssrep[, -1 ], cdata=TRUE )
summary(bdat2)

# selection of variables for BIFIEdata object
bdat1a <- BIFIEsurvey::BIFIEdata.select( bdat1, varnames=bdat1$varnames[ 1:7 ] )
# selection of variables and 1st, 2nd and 4th imputed datasets of BIFIEcdata object
bdat2a <- BIFIEsurvey::BIFIEdata.select( bdat2, varnames=bdat2$varnames[ 1:7 ],
                impdata.index=c(1,2,4) )
summary(bdat1a)
summary(bdat2a)

Conversion of a BIFIEdata Object into a svyrep Object in the survey Package (and the other way around)

Description

The function BIFIEdata2svrepdesign converts of a BIFIEdata object into a svyrep object in the survey package.

The function svrepdesign2BIFIEdata converts a svyrep object in the survey package into an object of class BIFIEdata.

Usage

BIFIEdata2svrepdesign(bifieobj, varnames=NULL, impdata.index=NULL)

svrepdesign2BIFIEdata(svrepdesign, varnames=NULL, cdata=FALSE)

Arguments

bifieobj

Object of class BIFIEdata

varnames

Optional vector with variable names

impdata.index

Selected indices of imputed datasets

svrepdesign

Object of class svyrep.design or svyimputationList

cdata

Logical inducating whether BIFIEdata object should be saved in compact format

Value

Function BIFIEdata2svrepdesign: Object of class svyrep.design or svyimputationList

Function svrepdesign2BIFIEdata: Object of class BIFIEdata

See Also

See the BIFIE.data function for creating objects of class BIFIEdata in BIFIEsurvey.

See the survey::svrepdesign function in the survey package.

Examples

## Not run: 
#############################################################################
# EXAMPLE 1: One dataset, TIMSS replication design
#############################################################################

data(data.timss3)
data(data.timssrep)

#--- create BIFIEdata object
bdat3 <- BIFIEsurvey::BIFIE.data.jack(data.timss3, jktype="JK_TIMSS")
summary(bdat3)

#--- create survey object directly in survey package
dat3a <- as.data.frame( cbind( data.timss3, data.timssrep ) )
RR <- ncol(data.timssrep) - 1       # number of jackknife zones
svydes3a <- survey::svrepdesign(data=dat3a, weights=~TOTWGT,type="JKn",
                 repweights='w_fstr[0-9]', scale=1,  rscales=rep(1,RR), mse=TRUE )
print(svydes3a)

#--- create survey object by converting the BIFIEdata object to survey
svydes3b <- BIFIEsurvey::BIFIEdata2svrepdesign(bdat3)

#--- convert survey object into BIFIEdata object
bdat3e <- BIFIEsurvey::svrepdesign2BIFIEdata(svrepdesign=svydes3b)

#*** compare results for the mean in Mathematics scores
mod1a <- BIFIEsurvey::BIFIE.univar( bdat3, vars="ASMMAT1")
mod1b <- survey::svymean( ~ ASMMAT1, design=svydes3a )
mod1c <- survey::svymean( ~ ASMMAT1, design=svydes3b )
lavmodel <- "ASMMAT1 ~ 1"
mod1d <- BIFIEsurvey::BIFIE.lavaan.survey(lavmodel, svyrepdes=svydes3b)

#- coefficients
coef(mod1a); coef(mod1b); coef(mod1c); coef(mod1d)[1]
#- standard errors
survey::SE(mod1a); survey::SE(mod1b); survey::SE(mod1c); sqrt(vcov(mod1d)[1,1])

#############################################################################
# EXAMPLE 2: Multiply imputed datasets, TIMSS replication design
#############################################################################

data(data.timss2)
data(data.timssrep)

#--- create BIFIEdata object
bdat4 <- BIFIEsurvey::BIFIE.data( data=data.timss2, wgt="TOTWGT",
              wgtrep=data.timssrep[,-1], fayfac=1)
print(bdat4)

#--- create object with imputed datasets in survey
datL <- mitools::imputationList( data.timss2 )
RR <- ncol(data.timssrep) - 1
weights <- data.timss2[[1]]$TOTWGT
repweights <-  data.timssrep[,-1]
svydes4a <- survey::svrepdesign(data=datL, weights=weights, type="other",
               repweights=repweights, scale=1,  rscales=rep(1,RR), mse=TRUE)
print(svydes4a)

#--- create BIFIEdata object with conversion function
svydes4b <- BIFIEsurvey::BIFIEdata2svrepdesign(bdat4)

#--- reconvert survey object into BIFIEdata object
bdat4c <- BIFIEsurvey::svrepdesign2BIFIEdata(svrepdesign=svydes4b)

#*** compare results for a mean
mod1a <- BIFIEsurvey::BIFIE.univar(bdat4, vars="ASMMAT")
mod1b <- mitools::MIcombine( with(svydes4a, survey::svymean( ~ ASMMAT, design=svydes4a )))
mod1c <- mitools::MIcombine( with(svydes4b, survey::svymean( ~ ASMMAT, design=svydes4b )))

# results
coef(mod1a); coef(mod1b); coef(mod1c)
survey::SE(mod1a); survey::SE(mod1b); survey::SE(mod1c)

## End(Not run)

Utility Functions in BIFIEsurvey

Description

Utility functions in BIFIEsurvey.

Usage

## Rubin rules for combining multiple imputation estimates
bifiesurvey_rcpp_rubin_rules(estimates, variances)

## computation of replication variance
bifiesurvey_rcpp_replication_variance(pars, pars_repl, fay_factor)

## statistical inference for nested multiple imputation
BIFIE_NMI_inference_parameters( parsM, parsrepM, fayfac, RR, Nimp, Nimp_NMI,
      comp_cov=FALSE)

Arguments

estimates

Vector

variances

Vector

pars

Matrix

pars_repl

Matrix

fay_factor

Vector

parsM

Matrix

parsrepM

Matrix

fayfac

Vector

RR

Numeric

Nimp

Integer

Nimp_NMI

Integer

comp_cov

Logical


An Rcpp Based Version of the table Function

Description

This is an Rcpp based version of the base::table function.

Usage

bifietable(vec, sort.names=FALSE)

Arguments

vec

A numeric or character vector

sort.names

An optional logical indicating whether values in the character vector should also be sorted in the table output

Value

Same output like base::table

See Also

base::table

Examples

data(data.timss1)
table( data.timss1[[1]][,"books"] )
BIFIEsurvey::bifietable( data.timss1[[1]][,"books"] )

Example Datasets for the BIFIEsurvey Package

Description

Some example datasets.

Usage

data(data.bifie01)

Format


Some PISA Datasets

Description

Some PISA datasets.

Usage

data(data.pisaNLD)

Format

The dataset data.pisaNLD is a data frame with 3992 observations on 405 variables which is a part of the Dutch PISA 2006 data.

Source

Downloaded from doi:10.18637/jss.v020.i05 (Fox, 2007).

References

Fox, J.-P. (2007). Multilevel IRT Modeling in practice with the package mlirt. Journal of Statistical Software, 20(5), 1-16. doi:10.18637/jss.v020.i05

Examples

## Not run: 
library(mitools)
library(survey)
library(intsvy)

#############################################################################
# EXAMPLE 1: Dutch PISA 2006 dataset
#############################################################################

data(data.pisaNLD)
data <- data.pisaNLD

#--- Create object of class BIFIEdata

# list variables with plausible values: These must be named
# as pv1math, pv2math, ..., pv5math, ...
pv_vars <- toupper( c("math", "math1", "math2", "math3", "math4",
             "read", "scie", "prob") )
# create 5 datasets including different sets of plausible values
dfr <- NULL
VV <- length(pv_vars)
Nimp <- 5           # number of plausible values
for (vv in 1:VV){
      vv1 <- pv_vars[vv]
      ind.vv1 <- which( colnames(data) %in% paste0("PV", 1:Nimp, vv1) )
      dfr2 <- data.frame( "variable"=paste0("PV", vv1), "var_index"=vv,
          "data_index"=ind.vv1, "impdata_index"=1:Nimp )
      dfr <- rbind( dfr, dfr2 )
}

sel_ind <- setdiff( 1:( ncol(data) ), dfr$data_index )
data0 <- data[, sel_ind ]
V0 <- ncol(data0)
newvars <- seq( V0+1, V0+VV )
datalist <- as.list( 1:Nimp )
for (ii in 1:Nimp ){
    dat1 <- data.frame( data0, data[, dfr[ dfr$impdata_index==ii, "data_index" ]])
    colnames(dat1)[ newvars ] <- paste0("PV",pv_vars)
    datalist[[ii]] <- dat1
}

# dataset with replicate weights
datarep <- data[, grep( "W_FSTR", colnames(data) ) ]
RR <- ncol(datarep)     # number of replicate weights

# create BIFIE object
bifieobj <- BIFIEsurvey::BIFIE.data( datalist, wgt=data[, "W_FSTUWT"],
                 wgtrep=datarep, fayfac=1 / RR / ( 1 - .5 )^2 )
# For PISA: RR=80 and therefore fayfac=1/20=.05
summary(bifieobj)

#--- Create BIFIEdata object immediately using BIFIE.data.jack function
bifieobj1 <- BIFIEsurvey::BIFIE.data.jack( data.pisaNLD, jktype="RW_PISA", cdata=TRUE)
summary(bifieobj1)

#--- Create object in survey package
datL <- mitools::imputationList(list( datalist[[1]],datalist[[2]],
                  datalist[[3]],datalist[[4]],datalist[[5]]) )
pisades <- survey::svrepdesign(ids=~ 1, weights=~W_FSTUWT, data=datL,
                    repweights="W_FSTR[0-9]+", type="Fay", rho=0.5, mse=TRUE)
print(pisades)

#++++++++++++++ some comparisons with other packages +++++++++++++++++++++++++++++++

#**** Model 1: Means for mathematics and reading
# BIFIEsurvey package
mod1a <- BIFIEsurvey::BIFIE.univar( bifieobj, vars=c("PVMATH", "PVREAD") )
summary(mod1a)

# intsvy package
mod1b <- intsvy::pisa.mean.pv(pvlabel="MATH", data=data.pisaNLD )
mod1b

# survey package
mod1c <- with( pisades, survey::svymean(PVMATH~1, design=pisades) )
res1c <- mitools::MIcombine(mod1c)
summary(res1c)

#**** Model 2: Linear regression
# BIFIEsurvey package
mod2a <- BIFIEsurvey::BIFIE.linreg( bifieobj, dep="PVMATH",
              pre=c("one","ANXMAT","HISEI"))
summary(mod2a)

# intsvy package
mod2b <- intsvy::pisa.reg.pv(pvlabel="MATH", x=c("ANXMAT","HISEI"), data=data.pisaNLD)
mod2b

# survey package
mod2c <- with( pisades, survey::svyglm(PVMATH~ANXMAT+HISEI, design=pisades) )
res2c <- mitools::MIcombine(mod2c)
summary(res2c)

## End(Not run)

Some Datasets for Testing Purposes

Description

Some datasets for testing purposes.

Usage

data(data.test1)

Format

The dataset data.test1 is a dataset with a stratified clustered sample of 2101 students nested within 89 classes and 4 strata. The format is

'data.frame': 2101 obs. of 16 variables:
$ idstud : num 10101 10102 10103 10104 10105 ...
$ idclass: num 101 101 101 101 101 101 101 101 101 101 ...
$ math : num 108 107 101 91 157 ...
$ engl : num 95.2 133.3 94.9 97.6 142.3 ...
$ germ : num 125 150 107 113 139 ...
$ stratum: num 1 1 1 1 1 1 1 1 1 1 ...
$ female : int 1 1 1 1 0 0 0 0 0 0 ...
$ age : num 14.6 14.3 14.8 14.6 14.5 ...
$ hisei : int 43 43 43 67 51 30 30 51 68 70 ...
$ paredu : int 2 2 1 4 5 2 1 5 7 7 ...
$ books : int 4 2 3 3 5 3 2 4 3 5 ...
$ satisf : int 5 4 6 7 6 5 7 3 6 6 ...
$ migrant: int 1 0 0 1 0 0 0 0 0 0 ...
$ wgtstud: num 20.9 20.9 20.9 20.9 20.9 ...
$ jkzone : num 101 101 101 101 101 101 101 101 101 101 ...
$ jkrep : num 0 0 0 0 0 0 0 0 0 0 ...


Dataset TIMSS 2011

Description

Example dataset TIMSS 2011

Usage

data(data.timss1)
data(data.timss1.ind)
data(data.timss2)
data(data.timssrep)
data(data.timss3)
data(data.timss4)

Format

The dataset data.timss1 is a list containing 5 imputed datasets. The dataset data.timss1.ind contains response indicators of these 5 imputed datasets in data.timss1.

The dataset data.timss2 is a list containing 5 datasets in which only plausible values are imputed, but student covariates are missing.

The dataset data.timssrep contains replicate weights of students.

The dataset data.timss3 is a TIMSS dataset with some missing student covariates and all 5 plausible values contained in one file.

The dataset data.timss4 is a list containing nested multiply imputed datasets, with 5 between-nest and 4 within-nest imputations.

Examples

## Not run: 
library(survey)
library(lavaan.survey)
library(intsvy)
library(mitools)

#############################################################################
# EXAMPLE 1: TIMSS dataset data.timss3 (one dataset including all PVs)
#############################################################################

data(data.timss2)
data(data.timss3)
data(data.timssrep)

# Analysis based on official 'single' datasets (data.timss3)
# There are 5 plausible values, but student covariates are not imputed.

#--- create object of class BIFIE data
bdat3 <- BIFIEsurvey::BIFIE.data(data.timss3, wgt=data.timss3$TOTWGT,
              wgtrep=data.timssrep[,-1], fayfac=1)
summary(bdat3)
# This BIFIEdata object contains one dataset in which all
# plausible values are included. This object can be used
# in analysis without plausible values.
# Equivalently, one can define bdat3 much simpler by
bdat3 <- BIFIEsurvey::BIFIE.data.jack(data.timss3, jktype="JK_TIMSS")
summary(bdat3)

#--- In the following, the object bdat4 is defined with 5 datasets
# referring to 5 plausible values.
bdat4 <- BIFIEsurvey::BIFIE.data.jack(data.timss3, pv_vars=c("ASMMAT","ASSSCI"),
               jktype="JK_TIMSS")
summary(bdat4)

#--- create object in survey package
dat3a <- as.data.frame( cbind( data.timss2[[1]], data.timssrep ) )
RR <- ncol(data.timssrep) - 1       # number of jackknife zones
svydes3 <- survey::svrepdesign(data=dat3a, weights=~TOTWGT, type="JKn",
                 repweights='w_fstr[0-9]', scale=1,  rscales=rep(1,RR), mse=TRUE)
summary(svydes3)

#--- create object with imputed datasets in survey
datL <- data.timss2
# include replicate weights in each dataset
for (ii in 1:5){
    dat1 <- datL[[ii]]
    dat1 <- cbind(  dat1, data.timssrep[,-1] )
    datL[[ii]] <- dat1
}
datL <- mitools::imputationList(list( datL[[1]],datL[[2]],datL[[3]],datL[[4]],datL[[5]]))
svydes4 <- survey::svrepdesign(data=datL, weights=~TOTWGT, type="JKn",
                   repweights='w_fstr[0-9]', scale=1,  rscales=rep(1,RR), mse=TRUE)
summary(svydes4)

#--- reconstruct data.timss3 for intsvy package. Plausible values must be labeled
# as PV01, PV02, ... and NOT PV1, PV2, ...
data.timss3a <- data.timss3
colnames(data.timss3a) <- gsub( "ASMMAT", "ASMMAT0", colnames(data.timss3a) )
colnames(data.timss3a) <- gsub( "ASSSCI", "ASSSCI0", colnames(data.timss3a) )

#***************************
# Model 1: Linear regression (no grouping variable)

#--- linear regression in survey
mod1a <-  survey::svyglm( scsci ~ migrant + books, design=svydes3)
summary(mod1a)

#--- regression with pirls.reg (intsvy)
mod1b <- intsvy::pirls.reg( y="scsci", x=c("migrant", "books" ), data=data.timss3)
mod1b

#---- regression with BIFIEsurvey
mod1c <- BIFIEsurvey::BIFIE.linreg( bdat3, dep="scsci", pre=c("one","migrant","books"))
summary(mod1c)

#--- regression with lavaan.survey package
lavmodel <- "
    scsci ~ migrant + books
    scsci ~ 1
    scsci ~~ scsci
        "
# fit in lavaan
lavaan.fit <- lavaan::lavaan( lavmodel, data=data.timss3, estimator="MLM")
summary(lavaan.fit)
# using all replicated weights
mod1d <- lavaan.survey::lavaan.survey(lavaan.fit=lavaan.fit, survey.design=svydes3 )
summary(mod1d)

#***************************
# Model 2: Linear regression (grouped by female)

#--- linear regression in survey
mod2a <- survey::svyglm( scsci ~ 0 + as.factor(female) + as.factor(female):migrant
               + as.factor(female):books, design=svydes3)
summary(mod2a)

#--- regression with pirls.reg (intsvy)
mod2b <- intsvy::pirls.reg( y="scsci", x=c("migrant", "books" ),
                 by="female", data=data.timss3)
mod2b[["0"]]   # regression coefficients female=0
mod2b[["1"]]   # regression coefficients female=1

#--- regression with BIFIEsurvey
mod2c <- BIFIEsurvey::BIFIE.linreg( bdat3, dep="scsci",
               pre=c("one","migrant","books"), group="female")
summary(mod2c)

#--- regression with lavaan.survey package
lavmodel <- "
    scsci ~ migrant + books
    scsci ~ 1
    scsci ~~ scsci
        "
# fit in lavaan
lavaan.fit <- lavaan::lavaan( lavmodel, data=data.timss3, group="female", estimator="MLM")
summary(lavaan.fit)
mod2d <- lavaan.survey::lavaan.survey(lavaan.fit=lavaan.fit, survey.design=svydes3 )
summary(mod2d)

#***************************
# Model 3: Linear regression with mathematics PVs
library(mitools)

#--- linear regression in survey
mod3a <- with(svydes4,  survey::svyglm( ASMMAT ~ migrant + books, design=svydes4 ) )
res3a <- mitools::MIcombine(mod3a)
summary(res3a)

#--- regression with pirls.reg.pv (intsvy)
mod3b <- intsvy::pirls.reg.pv( pvlabel="ASMMAT", x=c("migrant", "books" ),
             data=data.timss3a)

#--- regression with BIFIEsurvey
mod3c <- BIFIEsurvey::BIFIE.linreg( bdat4, dep="ASMMAT", pre=c("one","migrant","books"))
summary(mod3c)

#--- regression with lavaan.survey package
lavmodel <- "
    ASMMAT ~ migrant + books
    ASMMAT ~ 1
    ASMMAT ~~ ASMMAT
        "
# fit in lavaan
lavaan.fit <- lavaan::lavaan( lavmodel, data=data.timss3a, group="female", estimator="MLM")
summary(lavaan.fit)
mod3d <- lavaan.survey::lavaan.survey(lavaan.fit=lavaan.fit, survey.design=svydes4 )
summary(mod3d)

#############################################################################
# EXAMPLE 2: TIMSS dataset data.timss4 | Nested multiply imputed dataset
#############################################################################

data(data.timss4)
data(data.timssrep)

#**** create BIFIEdata object
bdat <- BIFIEsurvey::BIFIE.data( data.list=data.timss4, wgt=data.timss4[[1]][[1]]$TOTWGT,
               wgtrep=data.timssrep[, -1 ], NMI=TRUE, cdata=TRUE )
summary(bdat)

#**** Model 1: Linear regression for mathematics score
mod1 <- BIFIEsurvey::BIFIE.linreg( bdat, dep="ASMMAT", pre=c("one","books","migrant"))
summary(mod1)

#*** Model 2: Univariate statistics ?BIFIEsurvey::BIFIE.univar
mod2 <- BIFIEsurvey::BIFIE.univar( bdat, vars=c("ASMMAT","ASSSCI","books") )
summary(mod2)

## End(Not run)

Saving, Writing and Loading BIFIEdata Objects

Description

These functions save (save.BIFIEdata), write (write.BIFIEdata) or load (load.BIFIEdata) objects of class BIFIEdata.

The function load.BIFIEdata.files allows the creation of BIFIEdata objects by loading separate files of imputed datasets, replicate weights and a possible indicator dataset.

Usage

save.BIFIEdata(BIFIEdata, name.BIFIEdata, cdata=TRUE, varnames=NULL)

write.BIFIEdata( BIFIEdata, name.BIFIEdata, dir=getwd(), varnames=NULL,
    impdata.index=NULL, type="Rdata", ... )

load.BIFIEdata(filename, dir=getwd() )

load.BIFIEdata.files( files.imp, wgt, file.wgtrep, file.ind=NULL,
    type="Rdata",varnames=NULL, cdata=TRUE, dir=getwd(), ... )

Arguments

BIFIEdata

Object of class BIFIEdata

name.BIFIEdata

Name of BIFIEdata set to be saved

cdata

An optional logical indicating whether the dataset should be saved in a 'compact way'

varnames

Vector of variable names which should be saved. The default is to use all variables.

dir

Directory in which data files should be saved. The default is the working directory.

impdata.index

Vector of indices for selecting imputed datasets

type

Type of saved data. Options are Rdata (function base::save, csv (function utils::write.csv), csv2 (function utils::write.csv2), table (function utils::write.table), sav (function foreign::read.spss for reading sav files and function sjlabelled::write_spss for writing sav files).

...

Additional arguments to be passed to base::save, utils::write.csv, utils::write.csv2, utils::write.table, foreign::read.spss, sjlabelled::write_spss

filename

File name of BIFIEdata object

files.imp

Vector of file names of imputed datasets

wgt

Variable name of case weight

file.wgtrep

File name for dataset with replicate weights

file.ind

Optional. File name for dataset with response data indicators

Value

Saved R object and a summary in working directory or a loaded R object.

See Also

For creating objects of class BIFIEdata see BIFIE.data.

base::save, base::load

Examples

## Not run: 
#############################################################################
# EXAMPLE 1: Saving and loading BIFIE data objects
#############################################################################
data(data.timss1)
data(data.timssrep)

bifieobj <- BIFIEsurvey::BIFIE.data( data.list=data.timss1, wgt=data.timss1[[1]]$TOTWGT,
                 wgtrep=data.timssrep[, -1 ] )
summary(bifieobj)

# save bifieobj in a compact way
BIFIEsurvey::save.BIFIEdata( BIFIEdata=bifieobj, name.BIFIEdata="timss1_cdata" )
# save bifieobj in a non-compact way
BIFIEsurvey::save.BIFIEdata( BIFIEdata=bifieobj, name.BIFIEdata="timss1_data", cdata=FALSE)

# load this object with object name "bdat2"
bdat2 <- BIFIEsurvey::load.BIFIEdata( filename="timss1_data.Rdata" )
summary(bdat2)

# save bifieobj with selected variables
BIFIEsurvey::save.BIFIEdata( bifieobj, name.BIFIEdata="timss1_selectvars_cdata",
                     varnames=bifieobj$varnames[ c(1:7,13,12,9) ] )
# the same object, but use the non-compact way of saving
BIFIEsurvey::save.BIFIEdata( bifieobj, name.BIFIEdata="timss1_selectvars_data", cdata=FALSE,
                     varnames=bifieobj$varnames[ c(1:7,13,12,9) ] )

# load object timss1_cdata (in compact data format)
bdat3 <- BIFIEsurvey::load.BIFIEdata( filename="timss1_cdata.Rdata" )
summary(bdat3)
# save selected variables of object bdat3
BIFIEsurvey::save.BIFIEdata( bdat3, name.BIFIEdata="timss1_selectvars2_cdata",
                     varnames=bifieobj$varnames[ c(1:4,12,8) ] )

#############################################################################
# EXAMPLE 2: Writing BIFIEdata objects
#############################################################################

data(data.timss2)
data(data.timssrep)

# create compactBIFIEdata
bifieobj <- BIFIEsurvey::BIFIE.data( data.list=data.timss2, wgt=data.timss2[[1]]$TOTWGT,
                wgtrep=data.timssrep[, -1 ], cdata=TRUE)
summary(bifieobj)

# save imputed datasets in format csv2
BIFIEsurvey::write.BIFIEdata( bifieobj, name.BIFIEdata="timss2_save1", type="csv2", row.names=FALSE)

# save imputed datasets of BIFIEdata object in format table without column names
# and code missings as "."
BIFIEsurvey::write.BIFIEdata( bifieobj, name.BIFIEdata="timss2_save2", type="table",
                  col.names=FALSE, row.names=FALSE, na="." )

# save imputed datasets of  BIFIEdata object in format csv and select some variables
# and only the first three datasets
varnames <- c("IDSTUD","TOTWGT","female","books","lang","ASMMAT")
BIFIEsurvey::write.BIFIEdata( bifieobj, name.BIFIEdata="timss2_save3", type="csv",
                   impdata.index=1:3, varnames=varnames)

# save imputed datasets of BIFIEdata object in format Rdata, the R binary format
BIFIEsurvey::write.BIFIEdata( bifieobj, name.BIFIEdata="timss2_save4", type="Rdata"  )

# save imputed datasets in sav (SPSS) format
BIFIEsurvey::write.BIFIEdata( bifieobj, name.BIFIEdata="timss2_save5", type="sav" )

#############################################################################
# EXAMPLE 3: Loading BIFIEdata objects saved in separate files
#                   (no indicator dataset)
#############################################################################

# We assume that Example 2 is applied and we build on the saved files
# from this example.

#***--- read Rdata format
# extract files with imputed datasets and replicate weights
files.imp <- miceadds::grep.vec( c("timss2_save4__IMP", ".Rdata" ),
            list.files(getwd())  )$x
file.wgtrep <- miceadds::grep.vec( c("timss2_save4__WGTREP", ".Rdata" ),
            list.files(getwd())  )$x
# select some variables in varnames
varnames <- scan( nlines=1, what="character")
   IDSTUD   TOTWGT books lang migrant likesc  ASMMAT

# load files and create BIFIEdata object
bifieobj1 <- BIFIEsurvey::load.BIFIEdata.files( files.imp, wgt="TOTWGT", file.wgtrep,
                        type="Rdata", varnames=varnames )
summary(bifieobj1)

#***--- read csv2 format
files.imp <- miceadds::grep.vec( c("timss2_save1__IMP", ".csv" ),
                        list.files(getwd()) )$x
file.wgtrep <- miceadds::grep.vec( c("timss2_save1__WGTREP", ".csv" ),
                        list.files(getwd()) )$x
bifieobj2 <- BIFIEsurvey::load.BIFIEdata.files( files.imp, wgt="TOTWGT",
                    file.wgtrep, type="csv2" )
summary(bifieobj2)

#***--- read sav format
files.imp <- miceadds::grep.vec( c("timss2_save5__IMP", ".sav" ),
                        list.files(getwd()) )$x
file.wgtrep <- miceadds::grep.vec( c("timss2_save5__WGTREP", ".sav" ),
                        list.files(getwd()) )$x
bifieobj3 <- BIFIEsurvey::load.BIFIEdata.files( files.imp, wgt="TOTWGT",
                file.wgtrep, type="sav", to.data.frame=TRUE, use.value.labels=FALSE)
summary(bifieobj3)

#############################################################################
# EXAMPLE 4: Loading BIFIEdata objects saved in separate files
#                   (with an indicator dataset)
#############################################################################

data(data.timss1)
data(data.timss1.ind)
data(data.timssrep)

# create BIFIEdata object at first
bifieobj <- BIFIEsurvey::BIFIE.data( data.list=data.timss1, wgt="TOTWGT",
            wgtrep=data.timssrep[, -1 ] )
summary(bifieobj)

#--- save datasets for the purpose of the following example
write.BIFIEdata( BIFIEdata=bifieobj, name.BIFIEdata="timss1_ex", type="Rdata" )
# save indicator dataset
save( data.timss1.ind, file="timss1_ex__IND.Rdata" )

# grep file names
files.imp <- miceadds::grep.vec( c("timss1_ex__IMP", ".Rdata" ),
                      list.files(getwd()) )$x
file.wgtrep <- miceadds::grep.vec( c("timss1_ex__WGTREP", ".Rdata" ),
                      list.files(getwd()) )$x
file.ind <- miceadds::grep.vec( c("timss1_ex__IND", ".Rdata" ),
                      list.files(getwd()) )$x
# define variables for selection
varnames <- c("IDSTUD","TOTWGT","female","books","lang","ASMMAT")
# read files using indicator dataset
bifieobj2 <- BIFIEsurvey::load.BIFIEdata.files( files.imp, wgt="TOTWGT",
                  file.wgtrep=file.wgtrep, file.ind=file.ind, type="Rdata",
                  varnames=varnames)
summary(bifieobj2)

# read files without indicator dataset
bifieobj3 <- BIFIEsurvey::load.BIFIEdata.files( files.imp, wgt="TOTWGT",
                file.wgtrep=file.wgtrep, type="Rdata", varnames=varnames)
summary(bifieobj3)

# compare some descriptive statistics
res2 <- BIFIEsurvey::BIFIE.univar( bifieobj2, vars=c("books", "ASMMAT", "lang") )
res3 <- BIFIEsurvey::BIFIE.univar( bifieobj3, vars=c("books", "ASMMAT", "lang") )
summary(res2)
summary(res3)

## End(Not run)

Standard Errors of Estimated Parameters

Description

Outputs vector of standard errors of an estimated parameter vector.

Usage

se(object)

Arguments

object

Object for which S3 method vcov can be applied

Value

Vector

See Also

survey::SE

Examples

#############################################################################
# EXAMPLE 1: Toy example with lm function
#############################################################################

set.seed(906)
N <- 100
x <- seq(0,1,length=N)
y <- .6*x + stats::rnorm(N, sd=1)
mod <- stats::lm( y ~ x )
coef(mod)
vcov(mod)
se(mod)
summary(mod)