| 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: | 3.7-1 |
| Built: | 2024-07-25 04:59:50 UTC |
| Source: | https://github.com/alexanderrobitzsch/bifiesurvey |
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).
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):
Descriptive statistics: means and standard deviations (BIFIE.univar),
frequencies (BIFIE.freq),
crosstabs (BIFIE.crosstab)
Linear regression (BIFIE.linreg)
Logistic regression (BIFIE.logistreg)
Path models with measurement error correction for manifest
variables (BIFIE.pathmodel)
Two-level regression for hierarchical data (BIFIE.twolevelreg;
random slope model)
Statistical inference for derived parameters (BIFIE.derivedParameters)
Wald tests (BIFIE.waldtest) of model parameters based on
replicated statistics
User-defined R functions (BIFIE.by)
BIFIE [aut], Alexander Robitzsch [aut, cre], Konrad Oberwimmer [aut]
Maintainer: Alexander Robitzsch <[email protected]>
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 the survey, intsvy, EdSurvey, lavaan.survey, EVER and the eatRep packages.
## |----------------------------------------------------------------- ## | BIFIEsurvey 0.1-21 (2014-06-21) ## | Maintainer: Alexander Robitzsch <a.robitzsch at bifie.at > ## | http://www.bifie.at ## |----------------------------------------------------------------- ## .........................*,::; :,:; * .;*;. .,: ## :::::::::::::::::::::::::. ##+@ ##+# .@####+ ;+# * ## :::::::::::::::::::::::::. ###@ #### @@; :*;##** ## :::::::::::::::::::::::::. ###@ ##+# *##. .,, ## :::::::::::::::::::::::::. ###@ ::,: * ## ## :::::::::::::::::::::::::. ###@ * @@ ## :::::::::::::::::::::::::. ###@ * #@ ## :::::::::::::::::::::::::. ###@ #@ * ## :::::::::::::::::::::::::. ##@#,@###@ @@## * @@@+##@@@@ #@ *.@##### ## :::::::::::::::::::::::::. ####*@#####@.** #### @@#@@@#### #@ ;##+**#+@* ## :::::::::::::::::::::::::. ##@@##,,#+##@ #### @@ #@ .#@* * *## ## ::::,::::::::::::::::::::. ##+@@ ####, #### #@ #@ ##, #+* ## ::::**.::::::::::::::::::. ##+@, **,###@ #### #@ #@ @@ ;@; ## :::::* .::::::::::::::::. ##* #### #### #@ #@ .##@@@@@@@#+# ## :::::::* *,,:::::::::::. : ###@ #### #@ #@ ;##@@@@@@@@@@ ## :::::::::. **....* ,@# #### #### #@ #@ *@** ## ::::::::::::.* #### @###* #### #@ #@ *+#* ## ::::::::::::::::,,,,,,,::. #### #### #### #@ #@ **#* ## :::::::::::::::::::::::::. #### @### #### #@ #@ ,## ## :::::::::::::::::::::::::. ###@ * @##+ #### #@ #@ *#@ ## :::::::::::::::::::::::::* @@## ,#@## #### #@ #@ @# ## :::::::::::::::::::::::::* @+##, @###* #### #@ #@ *+#@ ## ::::::::::::::::::::::::. ###@: *###@ #### #@ #@ #@+ ## :::::::::::::::::::::::. **;@#@#@####@. #### #@ #@ *@#@:* * ## ::::::::::::::::::::::. ,@######@. ####* ## @+ *#####@## ## ::::::::::::::::::::.* * .*##*. * *** *. ** ;##+;.## |----------------------------------------------------------------- ## | BIFIEsurvey 0.1-21 (2014-06-21) ## | Maintainer: Alexander Robitzsch <a.robitzsch at bifie.at > ## | http://www.bifie.at ## |----------------------------------------------------------------- ## .........................*,::; :,:; * .;*;. .,: ## :::::::::::::::::::::::::. ##+@ ##+# .@####+ ;+# * ## :::::::::::::::::::::::::. ###@ #### @@; :*;##** ## :::::::::::::::::::::::::. ###@ ##+# *##. .,, ## :::::::::::::::::::::::::. ###@ ::,: * ## ## :::::::::::::::::::::::::. ###@ * @@ ## :::::::::::::::::::::::::. ###@ * #@ ## :::::::::::::::::::::::::. ###@ #@ * ## :::::::::::::::::::::::::. ##@#,@###@ @@## * @@@+##@@@@ #@ *.@##### ## :::::::::::::::::::::::::. ####*@#####@.** #### @@#@@@#### #@ ;##+**#+@* ## :::::::::::::::::::::::::. ##@@##,,#+##@ #### @@ #@ .#@* * *## ## ::::,::::::::::::::::::::. ##+@@ ####, #### #@ #@ ##, #+* ## ::::**.::::::::::::::::::. ##+@, **,###@ #### #@ #@ @@ ;@; ## :::::* .::::::::::::::::. ##* #### #### #@ #@ .##@@@@@@@#+# ## :::::::* *,,:::::::::::. : ###@ #### #@ #@ ;##@@@@@@@@@@ ## :::::::::. **....* ,@# #### #### #@ #@ *@** ## ::::::::::::.* #### @###* #### #@ #@ *+#* ## ::::::::::::::::,,,,,,,::. #### #### #### #@ #@ **#* ## :::::::::::::::::::::::::. #### @### #### #@ #@ ,## ## :::::::::::::::::::::::::. ###@ * @##+ #### #@ #@ *#@ ## :::::::::::::::::::::::::* @@## ,#@## #### #@ #@ @# ## :::::::::::::::::::::::::* @+##, @###* #### #@ #@ *+#@ ## ::::::::::::::::::::::::. ###@: *###@ #### #@ #@ #@+ ## :::::::::::::::::::::::. **;@#@#@####@. #### #@ #@ *@#@:* * ## ::::::::::::::::::::::. ,@######@. ####* ## @+ *#####@## ## ::::::::::::::::::::.* * .*##*. * *** *. ** ;##+;.
BIFIEdata Objects
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.
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)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)
bifieobj |
Object of class |
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 |
An object of class BIFIEdata saved in a non-compact
or compact way, see value cdata.
############################################################################# # 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]]$TOTWGT, wgtrep=data.timssrep[, -1 ]) summary(bifieobj) # define variables for which unique values should be extracted vars <- c( "female", "books","ASMMAT" ) # convert these variables from BIFIEdata object into a list of datasets bdatlist <- BIFIEsurvey::BIFIE.BIFIEdata2datalist( bifieobj, varnames=vars ) # look for unique values in first dataset for variables values <- lapply( bdatlist[[1]], FUN=function(vv){ sort( unique( vv ) ) } ) # number of unique values in first dataset Nvalues <- lapply( bdatlist[[1]], FUN=function(vv){ length( unique( vv ) ) } ) # number of unique values in all datasets Nvalues2 <- lapply( vars, FUN=function(vv){ #vv <- vars[1] unlist( lapply( bdatlist, FUN=function(dd){ length( unique( dd[,vv] ) ) } ) ) } ) # --> for extracting the number of unique values using BIFIE.by and a user # defined function see Example 1, Model 3 in "BIFIE.by" ## End(Not run)############################################################################# # EXAMPLE 1: BIFIEdata conversions using data.timss1 dataset ############################################################################# data(data.timss1) data(data.timssrep) # create BIFIEdata object bdat1 <- BIFIEsurvey::BIFIE.data( data.list=data.timss1, wgt=data.timss1[[1]]$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)
Computes statistics for user defined functions.
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,...)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,...)
BIFIEobj |
Object of class |
vars |
Vector of variables for which statistics should be computed |
userfct |
User defined function. This function must include
a matrix |
userparnames |
An optional vector of parameter names for the
value of |
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 |
digits |
Number of digits for rounding output |
... |
Further arguments to be passed |
A list with following entries
stat |
Data frame with statistics defined in |
output |
Extensive output with all replicated statistics |
... |
More values |
############################################################################# # 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$studwgt <- w mod0 <- lavaan::lavaan(lavmodel, data=X1, sampling.weights="TOTWGT") pars <- coef(mod0) # extract some fit statistics pars2 <- lavaan::fitMeasures(mod0) pars <- c(pars, pars2[c("cfi","tli")]) return(pars) } #* test function res0 <- userfct(X,w) userparnames <- names(res0) #* estimate lavaan model with replicated sampling weights res1 <- BIFIEsurvey::BIFIE.by( bifieobj, vars=vars, userfct=userfct, userparnames=userparnames, use_Rcpp=FALSE ) summary(res1) ## End(Not run)############################################################################# # EXAMPLE 1: Imputed TIMSS dataset ############################################################################# data(data.timss1) data(data.timssrep) # create BIFIE.dat object bifieobj <- BIFIEsurvey::BIFIE.data( data.list=data.timss1, wgt=data.timss1[[1]]$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)
Computes correlations and covariances
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, ...)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, ...)
BIFIEobj |
Object of class |
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 |
digits |
Number of digits for rounding output |
type |
If |
... |
Further arguments to be passed |
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 |
stats::cov.wt,
intsvy::timss.rho,
intsvy::timss.rho.pv,
Hmisc::rcorr,
miceadds::ma.wtd.corNA
############################################################################# # 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]]$TOTWGT, wgtrep=data.timssrep[, -1 ] ) # Correlations splitted by gender res1 <- BIFIEsurvey::BIFIE.correl( bdat, vars=c("lang", "books", "migrant" ), group="female", group_values=0:1 ) summary(res1) # Correlations splitted by gender: no statistical inference (se=FALSE) res1a <- BIFIEsurvey::BIFIE.correl( bdat, vars=c("lang", "books", "migrant" ), group="female", group_values=0:1, se=FALSE) summary(res1a)############################################################################# # EXAMPLE 1: Imputed TIMSS dataset ############################################################################# data(data.timss1) data(data.timssrep) # create BIFIE.dat object bdat <- BIFIEsurvey::BIFIE.data( data.list=data.timss1, wgt=data.timss1[[1]]$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)
Creates cross tabulations and computes some effect sizes.
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,...)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,...)
BIFIEobj |
Object of class |
vars1 |
Row variable |
vars2 |
Column variable |
vars_values1 |
Optional vector of values of variable |
vars_values2 |
Optional vector of values of variable |
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 |
digits |
Number of digits for rounding output |
... |
Further arguments to be passed |
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 |
output |
Extensive output with all replicated statistics |
... |
More values |
survey::svytable,
Hmisc::wtd.table
############################################################################# # 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]]$TOTWGT, wgtrep=data.timssrep[, -1 ] ) #--- Model 1: cross tabulation res1 <- BIFIEsurvey::BIFIE.crosstab( bifieobj, vars1="migrant", vars2="books", group="female" ) summary(res1)############################################################################# # EXAMPLE 1: Imputed TIMSS dataset ############################################################################# data(data.timss1) data(data.timssrep) # create BIFIE.dat object bifieobj <- BIFIEsurvey::BIFIE.data( data.list=data.timss1, wgt=data.timss1[[1]]$TOTWGT, wgtrep=data.timssrep[, -1 ] ) #--- Model 1: cross tabulation res1 <- BIFIEsurvey::BIFIE.crosstab( bifieobj, vars1="migrant", vars2="books", group="female" ) summary(res1)
BIFIEdata
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.
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,...)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,...)
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
|
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
|
pvpre |
Optional vector for prefixes of plausible values, see
|
cdata |
An optional logical indicating whether the |
NMI |
Optional logical indicating whether |
object |
Object of class |
x |
Object of class |
... |
Further arguments to be passed |
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 |
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 |
Nvars |
Number of variables |
variables |
Data frame including some informations about variables.
All transformations are saved in the column |
datalistM_ind |
Data frame with response indicators
(if |
datalistM_imputed |
Data frame with imputed values
(if |
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.
############################################################################# # 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)############################################################################# # 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)
BIFIE.data Object based on Bootstrap
Creates a BIFIE.data object based on bootstrap designs.
The sampling is done assuming independence of cases.
BIFIE.data.boot( data, wgt=NULL, pv_vars=NULL, Nboot=500, seed=.Random.seed, cdata=FALSE)BIFIE.data.boot( data, wgt=NULL, pv_vars=NULL, Nboot=500, seed=.Random.seed, cdata=FALSE)
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 |
Object of class BIFIEdata
## 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)## 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)
BIFIE.data Object with Jackknife Zones
Creates a BIFIE.data object for designs with jackknife zones,
especially for TIMSS/PIRLS and PISA studies.
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)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)
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 |
pv_vars |
An optional vector of plausible values which define multiply-imputed datasets. |
jktype |
Type of jackknife procedure for creating the |
jkzone |
Jackknife zones. If |
jkrep |
Jackknife replicate factors. If |
jkfac |
Factor for multiplying jackknife replicate weights.
If |
fayfac |
Fay factor for statistical inference. The default is set to |
wgtrep |
Variables in the dataset which refer to the replicate
weights. In case of |
pvpre |
Only applicable for |
ngr |
Number of randomly created groups in |
seed |
The simulation seed if |
cdata |
An optional logical indicating whether the |
Object of class BIFIEdata
############################################################################# # 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)############################################################################# # 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)
BIFIEdata Objects
Computes a data transformation for BIFIEdata objects.
BIFIE.data.transform( bifieobj, transform.formula, varnames.new=NULL )BIFIE.data.transform( bifieobj, transform.formula, varnames.new=NULL )
bifieobj |
Object of class |
transform.formula |
R formula object for data transformation. |
varnames.new |
Optional vector of names for new defined variables. |
An object of class BIFIEdata. Additional values are
varnames.added |
Added variables in data transformation |
varsindex.added |
Indices of added variables |
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$mean1M # The transformation formula can also be conveniently generated by string operations vars <- c("migrant", "female", "books", "lang" ) transform.formula2 <- as.formula( paste0( "~ 0 + I ( BIFIE.princomp( ~ ", paste0( vars, collapse="+" ), ", 3 ) )") ) ## > transform.formula2 ## ~ I(BIFIE.princomp(~migrant + female + books + lang, 3)) #**************************** #*** Transformation 10: Overwriting variables books and migrant bifieobj4 <- BIFIEsurvey::BIFIE.data.transform( bifieobj3, transform.formula=~ I( 1*(books >=1 ) ) + I(2*migrant), varnames.new=c("books","migrant") ) summary(bifieobj4) ## End(Not run)library(miceadds) ############################################################################# # EXAMPLE 1: Data transformations for TIMSS data ############################################################################# data(data.timss2) data(data.timssrep) # create BIFIEdata object bifieobj1 <- BIFIEsurvey::BIFIE.data( data.timss2, wgt=data.timss2[[1]]$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)
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.
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,...)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,...)
BIFIE.method |
Object of classes |
derived.parameters |
List with R formulas for derived parameters (see Examples for specification) |
type |
Only applies to |
object |
Object of class |
digits |
Number of digits for rounding decimals in output |
... |
Further arguments to be passed |
The distribution of derived parameters is derived by the direct calculation using original resampled parameters.
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 BIFIE.waldtest for multi-parameter tests.
See car::deltaMethod for the Delta method assuming that the multivariate
distribution of the parameters is
asymptotically normal.
############################################################################# # 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)############################################################################# # 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)
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.
BIFIE.ecdf( BIFIEobj, vars, breaks=NULL, quanttype=1, group=NULL, group_values=NULL ) ## S3 method for class 'BIFIE.ecdf' summary(object,digits=4,...)BIFIE.ecdf( BIFIEobj, vars, breaks=NULL, quanttype=1, group=NULL, group_values=NULL ) ## S3 method for class 'BIFIE.ecdf' summary(object,digits=4,...)
BIFIEobj |
Object of class |
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 |
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 |
digits |
Number of digits for rounding output |
... |
Further arguments to be passed |
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 |
Hmisc::wtd.ecdf,
Hmisc::wtd.quantile
############################################################################# # 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$yval, type="l", col="blue")############################################################################# # EXAMPLE 1: Imputed TIMSS dataset ############################################################################# data(data.timss1) data(data.timssrep) # create BIFIE.dat object bifieobj <- BIFIEsurvey::BIFIE.data( data.list=data.timss1, wgt=data.timss1[[1]]$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")
Computes absolute and relative frequencies.
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,...)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,...)
BIFIEobj |
Object of class |
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 |
digits |
Number of digits for rounding output |
... |
Further arguments to be passed |
A list with following entries
stat |
Data frame with frequency statistics |
output |
Extensive output with all replicated statistics |
... |
More values |
survey::svytable,
intsvy::timss.table,
Hmisc::wtd.table
############################################################################# # 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]]$TOTWGT, wgtrep=data.timssrep[, -1 ] ) # Frequencies for three variables res1 <- BIFIEsurvey::BIFIE.freq( bdat, vars=c("lang", "books", "migrant" ) ) summary(res1) # Frequencies splitted by gender res2 <- BIFIEsurvey::BIFIE.freq( bdat, vars=c("lang", "books", "migrant" ), group="female", group_values=0:1 ) summary(res2) # Frequencies splitted by gender and likesc res3 <- BIFIEsurvey::BIFIE.freq( bdat, vars=c("lang", "books", "migrant" ), group=c("likesc","female") ) summary(res3)############################################################################# # EXAMPLE 1: Imputed TIMSS dataset ############################################################################# data(data.timss1) data(data.timssrep) # create BIFIE.dat object bdat <- BIFIEsurvey::BIFIE.data( data.list=data.timss1, wgt=data.timss1[[1]]$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)
Computes a histogram with same output as in
graphics::hist.
Statistical inference is not conducted for this method.
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,...)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,...)
BIFIEobj |
Object of class |
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 |
x |
Object of class |
ask |
Optional logical whether it should be asked for new plots. |
... |
Further arguments to be passed |
A list with following entries
histobj |
List with objects of class |
output |
More extensive output |
... |
More values |
############################################################################# # 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$ASMMAT_female0, col="lightblue") # plot both histograms after each other plot( res1 ) # user-defined vector of breaks res2 <- BIFIEsurvey::BIFIE.hist( bifieobj, vars="ASMMAT", breaks=seq(0,900,10), group="female" ) plot( res2, col="orange")############################################################################# # EXAMPLE 1: Imputed TIMSS dataset ############################################################################# data(data.timss1) data(data.timssrep) # create BIFIE.dat object bifieobj <- BIFIEsurvey::BIFIE.data( data.list=data.timss1, wgt=data.timss1[[1]]$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")
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.
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,...)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,...)
lavmodel |
Model string in lavaan syntax |
svyrepdes |
Replication design object of class |
lavaan_fun |
Estimation funcion in lavaan. Can be |
lavaan_survey_default |
Logical indicating whether the lavaan.survey package should be used for statistical inference for multiply imputed datasets. |
object |
Object of class |
fit.measures |
Optional vector of fit measures used in
|
... |
Further arguments to be passed |
survey.function |
Function from the survey package |
digits |
Number of digits after decimal |
For BIFIE.lavaan.survey a list with following entries
lavfit |
Object of class |
fitstat |
Fit statistics from lavaan |
lavaan::lavaan,
lavaan.survey::lavaan.survey
## 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)## 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)
Computes linear regression.
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,...)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,...)
BIFIEobj |
Object of class |
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 |
formula |
An R formula object which can be applied instead of
providing |
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 |
digits |
Number of digits for rounding output |
... |
Further arguments to be passed |
A list with following entries
stat |
Data frame with unstandardized and standardized regression
coefficients, residual standard deviation and |
output |
Extensive output with all replicated statistics |
... |
More values |
Alternative implementations: survey::svyglm,
intsvy::timss.reg,
intsvy::timss.reg.pv,
stats::lm
See BIFIE.logistreg for logistic regression.
############################################################################# # 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)############################################################################# # 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)
Computes logistic regression. Explained variance is computed
by the approach of McKelvey and Zavoina.
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,...)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,...)
BIFIEobj |
Object of class |
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 |
formula |
An R formula object which can be applied instead of
providing |
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 |
digits |
Number of digits for rounding output |
... |
Further arguments to be passed |
A list with following entries
stat |
Data frame with regression coefficients |
output |
Extensive output with all replicated statistics |
... |
More values |
For linear regressions see BIFIE.linreg.
############################################################################# # 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]]$TOTWGT, wgtrep=data.timssrep[, -1 ] ) #**** Model 1: Logistic regression - prediction of migrational background res1 <- BIFIEsurvey::BIFIE.logistreg( BIFIEobj=bdat, dep="migrant", pre=c("one","books","lang"), group="female", se=FALSE ) summary(res1) ## Not run: # same model, but with formula specification and standard errors res1a <- BIFIEsurvey::BIFIE.logistreg( BIFIEobj=bdat, formula=migrant ~ books + lang, group="female" ) summary(res1a) ############################################################################# # SIMULATED EXAMPLE 2: Comparison of stats::glm and BIFIEsurvey::BIFIE.logistreg ############################################################################# #*** (1) simulate data set.seed(987) N <- 300 x1 <- stats::rnorm(N) x2 <- stats::runif(N) ypred <- -0.75+.2*x1 + 3*x2 y <- 1*( stats::plogis(ypred) > stats::runif(N) ) data <- data.frame( "y"=y, "x1"=x1, "x2"=x2 ) #*** (2) estimation logistic regression using glm mod1 <- stats::glm( y ~ x1 + x2, family="binomial") #*** (3) estimation logistic regression using BIFIEdata # create BIFIEdata object by defining 30 Jackknife zones bifiedata <- BIFIEsurvey::BIFIE.data.jack( data, jktype="JK_RANDOM", ngr=30 ) summary(bifiedata) # estimate logistic regression mod2 <- BIFIEsurvey::BIFIE.logistreg( bifiedata, formula=y ~ x1+x2 ) #*** (4) compare results summary(mod2) # BIFIE.logistreg summary(mod1) # glm ## End(Not run)############################################################################# # EXAMPLE 1: TIMSS dataset | Logistic regression ############################################################################# data(data.timss2) data(data.timssrep) # create BIFIE.dat object bdat <- BIFIEsurvey::BIFIE.data( data.list=data.timss2, wgt=data.timss2[[1]]$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)
Conducts a missing value analysis.
BIFIE.mva( BIFIEobj, missvars, covariates=NULL, se=TRUE ) ## S3 method for class 'BIFIE.mva' summary(object,digits=4,...)BIFIE.mva( BIFIEobj, missvars, covariates=NULL, se=TRUE ) ## S3 method for class 'BIFIE.mva' summary(object,digits=4,...)
BIFIEobj |
Object of class |
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 |
digits |
Number of digits for rounding output |
... |
Further arguments to be passed |
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 |
... |
More values |
############################################################################# # 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]]$TOTWGT, wgtrep=data.timssrep[, -1 ] ) # missing value analysis for "scsci" and "books" and three covariates res1 <- BIFIEsurvey::BIFIE.mva( BIFIEdata, missvars=c("scsci", "books" ), covariates=c("ASMMAT", "female", "ASSSCI") ) summary(res1) # missing value analysis without statistical inference and without covariates res2 <- BIFIEsurvey::BIFIE.mva( BIFIEdata, missvars=c("scsci", "books"), se=FALSE) summary(res2)############################################################################# # EXAMPLE 1: Imputed TIMSS dataset ############################################################################# data(data.timss1) data(data.timssrep) # create BIFIE.dat object BIFIEdata <- BIFIEsurvey::BIFIE.data( data.list=data.timss1, wgt=data.timss1[[1]]$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)
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).
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,...)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,...)
BIFIEobj |
Object of class |
lavaan.model |
String including the model specification in
lavaan syntax. |
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 |
digits |
Number of digits for rounding output |
... |
Further arguments to be passed |
The following conventions are used as parameter labels in the output.
Y~X is the regression coefficient of the regression from
on .
X->Z->Y denotes the path coefficient from to
passing the mediating variable .
X-+>Y denotes the total effect (of all paths) from to .
X-~>Y denotes the sum of all indirect effects from to .
The parameter suffix _stand refers to parameters for which
all variables are standardized.
A list with following entries
stat |
Data frame with unstandardized and standardized regression
coefficients, path coefficients, total and indirect effects,
residual variances, and |
output |
Extensive output with all replicated statistics |
... |
More values |
Buonaccorsi, J. P. (2010). Measurement error: Models, methods, and applications. CRC Press.
See the lavaan and lavaan.survey package.
For the lavaan syntax, see
lavaan::lavaanify and
TAM::lavaanify.IRT
## 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)## 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)
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).
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,...)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,...)
BIFIEobj |
Object of class |
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 |
wgtlevel2 |
Name of Level 2 weight variable |
wgtlevel1 |
Name of Level 1 weight variable. This is optional.
If it is not provided, |
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 |
se |
Optional logical indicating whether statistical inference
based on replication should be employed. In case of |
globconv |
Convergence criterion for maximum parameter change |
maxiter |
Maximum number of iterations |
object |
Object of class |
digits |
Number of digits for rounding output |
... |
Further arguments to be passed |
The implemented random slope model can be written as
where is the dependent variable,
includes the fixed effects predictors (specified by formula.fixed)
and includes the random effects predictors
(specified by formula.random). The random effects
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 and . It is assumed that
.
The different sources of variance are computed by formulas as
proposed in Snijders and Bosker (2012, Ch. 7).
A list with following entries
stat |
Data frame with coefficients and different sources of variance. |
output |
Extensive output with all replicated statistics |
... |
More values |
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.
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.
## 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)## 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)
Computes some univariate descriptive statistics (means and standard deviations).
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,...)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,...)
BIFIEobj |
Object of class |
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 |
digits |
Number of digits for rounding output |
... |
Further arguments to be passed |
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 BIFIE.univar.test for a test of equal means and
effect sizes and .
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
############################################################################# # 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)############################################################################# # 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)
Computes a Wald test which tests equality of means (univariate
analysis of variance). In addition, the and
effect sizes are computed.
BIFIE.univar.test(BIFIE.method, wald_test=TRUE) ## S3 method for class 'BIFIE.univar.test' summary(object,digits=4,...)BIFIE.univar.test(BIFIE.method, wald_test=TRUE) ## S3 method for class 'BIFIE.univar.test' summary(object,digits=4,...)
BIFIE.method |
Object of class |
wald_test |
Optional logical indicating whether a Wald test should be performed. |
object |
Object of class |
digits |
Number of digits for rounding output |
... |
Further arguments to be passed |
A list with following entries
stat.F |
Data frame with |
stat.eta |
Data frame with |
stat.dstat |
Data frame with Cohen's |
... |
More values |
############################################################################# # 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$groupval2, "#"), FUN=function(vv){vv[1]}) ) ) dstat[ ind, ] ## End(Not run)############################################################################# # EXAMPLE 1: Imputed TIMSS dataset - One grouping variable ############################################################################# data(data.timss1) data(data.timssrep) # create BIFIE.dat object bdat <- BIFIEsurvey::BIFIE.data( data.list=data.timss1, wgt=data.timss1[[1]]$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)
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.
BIFIE.waldtest(BIFIE.method, Cdes, rdes, type=NULL) ## S3 method for class 'BIFIE.waldtest' summary(object,digits=4,...)BIFIE.waldtest(BIFIE.method, Cdes, rdes, type=NULL) ## S3 method for class 'BIFIE.waldtest' summary(object,digits=4,...)
BIFIE.method |
Object of classes |
Cdes |
Design matrix |
rdes |
Design vector |
type |
Only applies to |
object |
Object of class |
digits |
Number of digits for rounding output |
... |
Further arguments to be passed |
The Wald test is conducted for a parameter vector ,
specifying the hypothesis . Statistical inference
is performed by using the and the statistic
(Enders, 2010, Ch. 8).
For objects of class bifie.univar, only hypotheses with respect
to means are implemented.
A list with following entries
stat.D |
Data frame with |
... |
More values |
Enders, C. K. (2010). Applied missing data analysis. Guilford Press.
survey::regTermTest,
survey::anova.svyglm,
car::linearHypothesis
############################################################################# # 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)############################################################################# # 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)
BIFIEdata
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.
BIFIEdata.select(bifieobj, varnames=NULL, impdata.index=NULL)BIFIEdata.select(bifieobj, varnames=NULL, impdata.index=NULL)
bifieobj |
Object of class |
varnames |
Variables chosen for the selection |
impdata.index |
Selected indices of imputed datasets |
An object of class BIFIEdata saved in a non-compact
or compact way, see value cdata
See BIFIE.data for creating BIFIEdata objects.
############################################################################# # 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)############################################################################# # 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)
BIFIEdata Object into a svyrep Object in the
survey Package (and the other way around)
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.
BIFIEdata2svrepdesign(bifieobj, varnames=NULL, impdata.index=NULL) svrepdesign2BIFIEdata(svrepdesign, varnames=NULL, cdata=FALSE)BIFIEdata2svrepdesign(bifieobj, varnames=NULL, impdata.index=NULL) svrepdesign2BIFIEdata(svrepdesign, varnames=NULL, cdata=FALSE)
bifieobj |
Object of class |
varnames |
Optional vector with variable names |
impdata.index |
Selected indices of imputed datasets |
svrepdesign |
Object of class |
cdata |
Logical inducating whether |
Function BIFIEdata2svrepdesign:
Object of class svyrep.design or svyimputationList
Function svrepdesign2BIFIEdata: Object of class BIFIEdata
See the BIFIE.data function for creating objects of class
BIFIEdata in BIFIEsurvey.
See the survey::svrepdesign function in
the survey package.
## 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)## 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.
## 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)## 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)
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 |
table Function
This is an Rcpp based version of the
base::table function.
bifietable(vec, sort.names=FALSE)bifietable(vec, sort.names=FALSE)
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 |
Same output like base::table
data(data.timss1) table( data.timss1[[1]][,"books"] ) BIFIEsurvey::bifietable( data.timss1[[1]][,"books"] )data(data.timss1) table( data.timss1[[1]][,"books"] ) BIFIEsurvey::bifietable( data.timss1[[1]][,"books"] )
Some example datasets.
data(data.bifie01)data(data.bifie01)
The dataset data.bifie01 contains data of 4th Grade Austrian
students from the TIMSS 2011 study.
Some PISA datasets.
data(data.pisaNLD)data(data.pisaNLD)
The dataset data.pisaNLD is a data frame with 3992 observations on 405 variables
which is a part of the Dutch PISA 2006 data.
Downloaded from doi:10.18637/jss.v020.i05 (Fox, 2007).
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
## 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)## 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.
data(data.test1)data(data.test1)
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 ...
Example dataset TIMSS 2011
data(data.timss1) data(data.timss1.ind) data(data.timss2) data(data.timssrep) data(data.timss3) data(data.timss4)data(data.timss1) data(data.timss1.ind) data(data.timss2) data(data.timssrep) data(data.timss3) data(data.timss4)
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.
## 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)## 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)
BIFIEdata Objects
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.
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(), ... )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(), ... )
BIFIEdata |
Object of class |
name.BIFIEdata |
Name of |
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 |
... |
Additional arguments to be passed to
|
filename |
File name of |
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 |
Saved R object and a summary in working directory or a loaded R object.
For creating objects of class BIFIEdata see BIFIE.data.
## 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)## 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)
Outputs vector of standard errors of an estimated parameter vector.
se(object)se(object)
object |
Object for which S3 method |
Vector
############################################################################# # 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)############################################################################# # 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)