mice.nmi.Rd
Performs nested multiple imputation (Rubin, 2003) for the functions
mice::mice
and mice.1chain
.
The function mice.nmi
generates an object of class mids.nmi
.
mice.nmi(datlist, type="mice", ...) # S3 method for mids.nmi summary(object, ...) # S3 method for mids.nmi print(x, ...)
datlist | List of datasets for which nested multiple imputation should be applied |
---|---|
type | Imputation model: |
... | Arguments to be passed to |
object | Object of class |
x | Object of class |
Object of class mids.nmi
with entries
List of nested multiply imputed datasets whose entries
are of class mids
or mids.1chain
.
Number of between and within imputations.
Rubin, D. B. (2003). Nested multiple imputation of NMES via partially incompatible MCMC. Statistica Neerlandica, 57(1), 3-18. doi: 10.1111/1467-9574.00217
For imputation models see mice::mice
and mice.1chain
.
Functions for analyses of nested multiply imputed datasets:
complete.mids.nmi
, with.mids.nmi
,
pool.mids.nmi
if (FALSE) { ############################################################################# # EXAMPLE 1: Nested multiple imputation for TIMSS data ############################################################################# 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 <- miceadds::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 #*************** # (1) nested multiple imputation using mice imp1 <- miceadds::mice.nmi( datlist, method=impMethod, predictorMatrix=predM, m=4, maxit=3 ) summary(imp1) #*************** # (2) nested multiple imputation using mice.1chain imp2 <- miceadds::mice.nmi( datlist, method=impMethod, predictorMatrix=predM, Nimp=4, burnin=10,iter=22, type="mice.1chain") summary(imp2) }