miceadds (version 2.2-0)

mice.impute.2lonly.function: Imputation at Level 2 (in miceadds)

Description

The imputation method mice.impute.2lonly.function is a general imputation function for level 2 imputation which allow any defined imputation function at level 1 in mice.

Usage

mice.impute.2lonly.function(y, ry, x, type, imputationFunction, ...)

Arguments

y

Incomplete data vector of length n

ry

Vector of missing data pattern (FALSE=missing, TRUE=observed)

x

Matrix (n x p) of complete covariates. Only numeric variables are permitted for usage of this function.

type

Group identifier must be specified by '-2'. Predictors must be specified by '1'.

imputationFunction

Imputation function for mice. Any imputation method which is defined at level 1 can be used for level 2 imputation.

...

Other named arguments.

Value

A vector of length nmis with imputations.

See Also

See mice::mice.impute.2lonly.norm and the mice::mice.impute.2lonly.pmm function.

See also the jomo package (jomo::jomo2) for joint multilevel imputation of level 1 and level 2 variables.

Examples

## Not run: 
# #############################################################################
# # EXAMPLE 1: Imputation of level 2 variables
# #############################################################################
# 
# #**** Simulate some data
# # x,y ... level 1 variables
# # v,w ... level 2 variables
# 
# G <- 250            # number of groups
# n <- 20             # number of persons
# beta <- .3          # regression coefficient
# rho <- .30          # residual intraclass correlation
# rho.miss <- .10     # correlation with missing response
# missrate <- .50     # missing proportion
# y1 <- rep( rnorm( G , sd = sqrt( rho ) ) , each=n ) + rnorm(G*n , sd = sqrt( 1 - rho ))
# w <- rep( round( rnorm(G ) , 2 ) , each=n )
# v <- rep( round( runif( G , 0 , 3 ) ) , each=n )
# x <-  stats::rnorm( G*n )
# y <- y1 + beta  * x + .2 * w + .1 * v
# dfr0 <- dfr <- data.frame( "group" = rep(1:G , each=n ) , "x" = x , "y" = y , 
# 		"w" = w , "v" = v )
# dfr[ rho.miss * x + rnorm( G*n , sd = sqrt( 1 - rho.miss ) ) < qnorm( missrate ) , 
# 		"y" ] <- NA
# dfr[ rep( rnorm(G) , each=n ) < qnorm( missrate ) , "w" ] <- NA
# dfr[ rep( rnorm(G) , each=n ) < qnorm( missrate ) , "v" ] <- NA
# 
# #....
# # empty mice imputation
# imp0 <- mice( as.matrix(dfr)  , maxit=0 )
# predM <- imp0$predictorMatrix
# impM <- imp0$method
# 
# #...
# # multilevel imputation 
# predM1 <- predM
# predM1[c("w","y","v"),"group"] <- -2
# predM1["y","x"] <- 1        # fixed x effects imputation
# impM1 <- impM
# impM1[c("y","w","v")] <- c("2l.continuous" , "2lonly.function" , "2lonly.function" )
# # define imputation functions 
# imputationFunction <- list( "w" = "sample" , "v" = "pmm5" )
# 
# # do imputation
# imp1 <- mice::mice( as.matrix(dfr) , m = 1 , predictorMatrix = predM1 ,
#            imputationMethod = impM1 , maxit = 5, 
#            imputationFunction = imputationFunction )
# ## End(Not run)