mice.nmi.RdPerforms 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) }