# Adding a Standardized Variable to a List of Multiply Imputed Datasets or a Single Datasets Adds a standardized variable to a list of multiply imputed datasets or a single dataset. This function extends `base::scale` for a data frame to a list of multiply imputed datasets. ```r scale_datlist(datlist, orig_var, trafo_var, weights=NULL, M=0, SD=1, digits=NULL) ``` ## Arguments - `datlist`: A data frame, a list of multiply imputed datasets of one of the classes `datlist` or `imputationList` or a list of nested multiply imputed datasets of one of the classes `nested_datlist` or `NestedImputationList`. - `orig_var`: Vector with names of the variables to be transformed - `trafo_var`: Vector with names of the standardized variables - `weights`: Optional vector of sample weights. Alternatively, the `weights` can also be a string indicating the variable used from `datlist`. - `M`: Mean of the transformed variable - `SD`: Standard deviation of the transformed variable - `digits`: Number of digits used for rounding the standardized variable ## Returns A vector or a matrix ## See Also `base::scale`, `ma.scale2` ## Examples ```r ## Not run: ############################################################################# # EXAMPLE 1: Standardized variables in list of multiply imputed datasets ############################################################################# data(data.ma02) datlist <- data.ma02 #--- object of class 'datlist' datlist <- miceadds::datlist_create( datlist ) # mean and SD of variable hisei miceadds::ma.wtd.meanNA(data=datlist, weights=datlist[[1]]$studwgt, vars="hisei" ) mean( unlist( lapply( datlist, FUN=function(data){ stats::weighted.mean( data$hisei, data$studwgt ) } ) ) ) miceadds::ma.wtd.sdNA(data=datlist, weights=datlist[[1]]$studwgt, vars="hisei" ) mean( unlist( lapply( datlist, FUN=function(data){ sqrt( Hmisc::wtd.var( data$hisei, data$studwgt ) ) } ) ) ) # standardize variable hisei to M=100 and SD=15 datlist1a <- miceadds::scale_datlist( datlist=datlist, orig_var="hisei", trafo_var="hisei100", weights=datlist[[1]]$studwgt, M=100, SD=15 ) # check mean and SD miceadds::ma.wtd.meanNA(data=datlist1a, weights=datlist[[1]]$studwgt, vars="hisei100") miceadds::ma.wtd.sdNA(data=datlist1a, weights=datlist[[1]]$studwgt, vars="hisei100") #--- do standardization for unweighted sample with books <=3 # -> define a weighting variable at first datlist0 <- mitools::imputationList( datlist ) datlist2a <- miceadds::within.imputationList( datlist0, { # define weighting variable wgt_books <- 1 * ( books <=3 ) } ) # standardize variable hisei to M=100 and SD=15 with respect to weighting variable datlist2b <- miceadds::scale_datlist( datlist=datlist2a, orig_var="hisei", trafo_var="hisei100", weights="wgt_books", M=100, SD=15 ) # check mean and SD (groupwise) miceadds::ma.wtd.meanNA(data=datlist1a, weights=datlist[[1]]$studwgt, vars="hisei100") miceadds::ma.wtd.sdNA(data=datlist1a, weights=datlist[[1]]$studwgt, vars="hisei100") #--- transformation for a single dataset dat0 <- datlist[[1]] dat0a <- miceadds::scale_datlist( datlist=dat0, orig_var="hisei", trafo_var="hisei100", weights=dat0$studwgt, M=100, SD=15 ) stats::weighted.mean( dat0a[,"hisei"], w=dat0a$studwgt ) stats::weighted.mean( dat0a[,"hisei100"], w=dat0a$studwgt ) sqrt( Hmisc::wtd.var( dat0a[,"hisei100"], weights=dat0a$studwgt ) ) #--- Standardizations for objects of class imputationList datlist2 <- mitools::imputationList(datlist) # object class conversion datlist2a <- miceadds::scale_datlist( datlist=datlist2, orig_var="hisei", trafo_var="hisei100", weights=datlist[[1]]$studwgt, M=100, SD=15 ) ############################################################################# # EXAMPLE 2: Standardized variables in list of nested multiply imputed datasets ############################################################################# # nested multiply imputed dataset in BIFIEsurvey package data(data.timss4, package="BIFIEsurvey") datlist <- data.timss4 wgt <- datlist[[1]][[1]]$TOTWGT # class nested.datlist imp1 <- miceadds::nested.datlist_create( datlist ) # class NestedImputationList imp2 <- miceadds::NestedImputationList( datlist ) # standardize variable scsci imp1a <- miceadds::scale_datlist( datlist=imp1, orig_var="scsci", trafo_var="zscsci", weights=wgt) # check descriptives miceadds::ma.wtd.meanNA( imp1a, weights=wgt, vars=c("scsci", "zscsci" ) ) miceadds::ma.wtd.sdNA( imp1a, weights=wgt, vars=c("scsci", "zscsci" ) ) ############################################################################# # EXAMPLE 3: Standardization of variables for imputed data in mice package ############################################################################# data(nhanes, package="mice") set.seed(76) #--- impute nhanes data imp <- mice::mice(nhanes) #--- convert into datlist datlist <- miceadds::mids2datlist(imp) #--- scale datlist (all variables) vars <- colnames(nhanes) sdatlist <- miceadds::scale_datlist(datlist, orig_var=vars, trafo_var=paste0("z",vars) ) #--- reconvert to mids object imp2 <- miceadds::datlist2mids(sdatlist) #*** compare descriptive statistics of objects round( miceadds::mean0( mice::complete(imp, action=1) ), 2 ) round( miceadds::mean0( mice::complete(imp2, action=1) ), 2 ) ## End(Not run) ```

scale_datlist function