miceadds (version 1.6-0)

datlist_create: Creates Objects of Class datlist or nested.datlist

Description

Creates objects of class datlist or nested.datlist.

Usage

datlist_create(datasets)

nested.datlist_create(datasets)

Arguments

datasets

For datlist_create: List of datasets, objects of class imputationList, mids, mids.1chain, For nested.datlist_create: nested list of datasets, NestedImputationList, mi

Value

• Object of class datlist or nested.datlist

Examples

## The function datlist_create is currently defined as
function (datasets) 
{
    class(datasets) <- "datlist"
    return(datasets)
  }
  
#############################################################################
# EXAMPLE 1: Create object of class datlist
#############################################################################

library(BIFIEsurvey)
data(data.timss2 , package="BIFIEsurvey" )
datlist <- data.timss2      

# class datlist
obj1 <- datlist_create(data.timss2)

#############################################################################
# EXAMPLE 2: Multiply imputed datasets: Different object classes
#############################################################################

library(mice)
data(nhanes2, package="mice")
set.seed(990)

# nhanes2 data imputation
imp1 <- mice.1chain( nhanes2 , burnin=5 , iter=25 , Nimp=5 )
# object of class datlist
imp2 <- mids2datlist(imp1)
# alternatively, one can use datlist_create
imp2b <- datlist_create(imp1)
# object of class imputationList
imp3 <- mitools::imputationList(imp2)
# retransform object in class datlist
imp2c <- datlist_create(imp3)
str(imp2c)

#############################################################################
# EXAMPLE 3: Nested multiply imputed datasets
#############################################################################

library(BIFIEsurvey)
data(data.timss2 , package="BIFIEsurvey" )
datlist <- data.timss2      
   # list of 5 datasets containing 5 plausible values

#** define imputation method and predictor matrix
data <- datlist[[1]]
V <- ncol(data)
# variables
vars <- colnames(data)
# variables not used for imputation
vars_unused <- scan.vec("IDSTUD TOTWGT  JKZONE  JKREP" )

#- define imputation method
impMethod <- rep("norm" , V )
names(impMethod) <- vars
impMethod[ vars_unused ] <- ""

#- define predictor matrix
predM <- matrix( 1 , V , V )
colnames(predM) <- rownames(predM) <- vars
diag(predM) <- 0
predM[ , vars_unused ] <- 0

# object of class nmi
imp1 <- mice.nmi( datlist , imputationMethod=impMethod , predictorMatrix=predM, 
                m=4 , maxit=3 )
# object of class nested.datlist
imp2 <- mids2datlist(imp1)
# object of class NestedImputationList
imp3 <- NestedImputationList(imp2)
# redefine class nested.datlist
imp4 <- nested.datlist_create(imp3)