mice (version 2.14)

mice.impute.2l.pan: Imputation by a Two-Level Normal Model using pan

Description

Imputes univariate missing data using a two-level normal model with homogeneous within group variances. Aggregated group effects (i.e. group means) can be automatically created and included as predictors in the two-level regression (see argument type). This function needs the pan package.

Usage

mice.impute.2l.pan(y, ry, x, type, intercept=TRUE, paniter=500 , groupcenter.slope=FALSE , ...)
mice.impute.2L.pan(y, ry, x, type, intercept=TRUE, paniter=500 , groupcenter.slope=FALSE , ...)

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.

type

Vector of length ncol(x) identifying random and class variables. Random effects are identified by a '2'. The group variable (only one is allowed) is coded as '-2'. Random effects also include the fixed effect. If for a co

intercept

Logical determining whether the intercept is automatically added.

paniter

Number of iterations in pan. Default is 500.

groupcenter.slope

If TRUE, in case of group means (type is '3' or'4') group mean centering for these predictors are conducted before doing imputations. Default is FALSE.

...

Other named arguments.

Value

• A vector of length nmis with imputations.

Details

Implements the Gibbs sampler for the linear two-level model with homogeneous within group variances which is a special case of a multivariate linear mixed effects model (Schafer & Yucel, 2002). For a two-level imputation with heterogeneous within-group variances see {mice.impute.2l.norm}.

References

Schafer J L, Yucel RM (2002). Computational strategies for multivariate linear mixed-effects models with missing values. Journal of Computational and Graphical Statistics. 11, 437-457.

Van Buuren, S., Groothuis-Oudshoorn, K. (2011). mice: Multivariate Imputation by Chained Equations in R. Journal of Statistical Software, 45(3), 1-67. http://www.jstatsoft.org/v45/i03/

See Also

mice.impute.2l.norm

Examples

###################################
# simulate some data
# two-level regression model with fixed slope

# number of groups
G <- 250
# number of persons
n <- 20
# regression parameter
beta <- .3
# intraclass correlation
rho <- .30
# correlation with missing response
rho.miss <- .10
# missing proportion
missrate <- .50
y1 <- rep( rnorm( G , sd = sqrt( rho ) ) , each=n ) + rnorm(G*n , sd = sqrt( 1 - rho )) 
x <-  rnorm( G*n )
y <- y1 + beta  * x
dfr0 <- dfr <- data.frame( "group" = rep(1:G , each=n ) , "x" = x , "y" = y )
dfr[ rho.miss * x + rnorm( G*n , sd = sqrt( 1 - rho.miss ) ) < qnorm( missrate ) , "y" ] <- NA

#.....
# empty imputation in mice
imp0 <- mice( as.matrix(dfr)  , maxit=0 )
predM <- imp0$predictorMatrix
impM <- imp0$method

#...
# specify predictor matrix and imputationMethod
predM1 <- predM
predM1["y","group"] <- -2
predM1["y","x"] <- 1        # fixed x effects imputation
impM1 <- impM
impM1["y"] <- "2l.pan"

# multilevel imputation
imp1 <- mice( as.matrix( dfr ) , m = 1 , predictorMatrix = predM1 , 
            imputationMethod = impM1 , maxit=1 )
# multilevel analysis
library(lme4)
mod <- lmer( y ~ ( 1 + x | group) + x , data = complete(imp1) )
summary(mod)

############################################
# Examples of predictorMatrix specification

# random x effects
# predM1["y","x"] <- 2

# fixed x effects and group mean of x
# predM1["y","x"] <- 3        

# random x effects and group mean of x
# predM1["y","x"] <- 4