R: Imputation by a two-level normal model using 'pan'

mice.impute.2l.pan {mice}

R Documentation

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, ...)

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 covariates X1 group means shall be calculated and included as further fixed effects choose '3'. In addition to the effects in '3', specification '4' also includes random effects of X1.
`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.

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. The random intercept is automatically added in mice.impute.2l.norm().

Value

A vector of length nmis with imputations.

Author(s)

Alexander Robitzsch (Federal Institute for Education Research, Innovation, and Development of the Austrian School System, Salzburg, Austria), a.robitzsch@bifie.at

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/

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

[Package mice version 2.22 Index]

Imputation by a two-level normal model using pan