mice (version 3.4.0)

mice.impute.2lonly.norm: Imputation at level 2 by Bayesian linear regression

Description

Imputes univariate missing data at level 2 using Bayesian linear regression analysis. Variables are level 1 are aggregated at level 2. The group identifier at level 2 must be indicated by type=-2 in the predictorMatrix.

Usage

mice.impute.2lonly.norm(y, ry, x, type, wy = NULL, ...)

Arguments

y

Vector to be imputed

ry

Logical vector of length length(y) indicating the the subset y[ry] of elements in y to which the imputation model is fitted. The ry generally distinguishes the observed (TRUE) and missing values (FALSE) in y.

x

Numeric design matrix with length(y) rows with predictors for y. Matrix x may have no missing values.

type

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

wy

Logical vector of length length(y). A TRUE value indicates locations in y for which imputations are created.

...

Other named arguments.

Value

A vector of length nmis with imputations.

Details

This function allows in combination with mice.impute.2l.pan switching regression imputation between level 1 and level 2 as described in Yucel (2008) or Gelman and Hill (2007, p. 541).

References

Gelman, A. and Hill, J. (2007). Data analysis using regression and multilevel/hierarchical models. Cambridge, Cambridge University Press.

Yucel, RM (2008). Multiple imputation inference for multivariate multilevel continuous data with ignorable non-response. Philosophical Transactions of the Royal Society A, 366, 2389-2404.

See Also

mice.impute.norm, mice.impute.2lonly.pmm, mice.impute.2l.pan

Other univariate 2lonly functions: mice.impute.2lonly.mean, mice.impute.2lonly.pmm

Examples

# NOT RUN {
##################################################
# 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 <-  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.pan" , "2lonly.norm" , "2lonly.pmm" )

# y ... imputation using pan
# w ... imputation at level 2 using norm
# v ... imputation at level 2 using pmm

imp1 <- mice( as.matrix( dfr ) , m = 1 , predictorMatrix = predM1 , 
           imputationMethod = impM1 , maxit=1 , paniter=500)
   
# }