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% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/mice.impute.2l.pan.R
\name{mice.impute.2l.pan}
\alias{mice.impute.2l.pan}
\alias{2l.pan}
\title{Imputation by a two-level normal model using \code{pan}}
\usage{
mice.impute.2l.pan(
y,
ry,
x,
type,
intercept = TRUE,
paniter = 500,
groupcenter.slope = FALSE,
...
)
}
\arguments{
\item{y}{Incomplete data vector of length \code{n}}
\item{ry}{Vector of missing data pattern (\code{FALSE}=missing,
\code{TRUE}=observed)}
\item{x}{Matrix (\code{n} x \code{p}) of complete covariates.}
\item{type}{Vector of length \code{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.}
\item{intercept}{Logical determining whether the intercept is automatically
added.}
\item{paniter}{Number of iterations in \code{pan}. Default is 500.}
\item{groupcenter.slope}{If \code{TRUE}, in case of group means (\code{type}
is '3' or'4') group mean centering for these predictors are conducted before
doing imputations. Default is \code{FALSE}.}
\item{...}{Other named arguments.}
}
\value{
A vector of length \code{nmis} with imputations.
}
\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 \code{type}). This function needs the
\code{pan} package.
}
\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 \code{\link{mice.impute.2l.norm}}. %
The random intercept is automatically added in %
\code{mice.impute.2l.norm()}.
}
\note{
This function does not implement the \code{where} functionality. It
always produces \code{nmis} imputation, irrespective of the \code{where}
argument of the \code{mice} function.
}
\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 method
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,
method = 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
}
\references{
Schafer J L, Yucel RM (2002). Computational strategies for multivariate
linear mixed-effects models with missing values. \emph{Journal of
Computational and Graphical Statistics}. \bold{11}, 437-457.
Van Buuren, S., Groothuis-Oudshoorn, K. (2011). \code{mice}: Multivariate
Imputation by Chained Equations in \code{R}. \emph{Journal of Statistical
Software}, \bold{45}(3), 1-67. \doi{10.18637/jss.v045.i03}
}
\seealso{
Other univariate-2l:
\code{\link{mice.impute.2l.bin}()},
\code{\link{mice.impute.2l.lmer}()},
\code{\link{mice.impute.2l.norm}()}
}
\author{
Alexander Robitzsch (IPN - Leibniz Institute for Science and
Mathematics Education, Kiel, Germany), \email{robitzsch@ipn.uni-kiel.de}
Alexander Robitzsch (IPN - Leibniz Institute for Science and
Mathematics Education, Kiel, Germany), \email{robitzsch@ipn.uni-kiel.de}.
}
\concept{univariate-2l}
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