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mice.impute.2l.contextual.pmm

Imputation by Predictive Mean Matching or Normal Linear Regression with Contextual Variables

Description

This imputation method imputes a variable using linear regression with predictive mean matching as the imputation method. Including a contextual effects means that an aggregated variable at a cluster level is included as a further covariate.

Usage

mice.impute.2l.contextual.pmm(y, ry, x, type, imputationWeights=NULL,
     interactions=NULL, quadratics=NULL, pls.facs=NULL, ...)

mice.impute.2l.contextual.norm(y, ry, x, type, ridge=10^(-5),
   imputationWeights=NULL, interactions=NULL, quadratics=NULL, pls.facs=NULL, ...)

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`	Type of predictor variables. `type=-2` refers to the cluster variable, `type=2` denotes a variable for which also a contextual effect is included and `type=1` denotes all other variables which are included as 'ordinary' predictors.
`imputationWeights`	Optional vector of sample weights
`interactions`	Vector of variable names used for creating interactions
`quadratics`	Vector of variable names used for creating quadratic terms
`pls.facs`	Number of factors used in partial least dimension reduction (if requested)
`...`	Further arguments to be passed
`ridge`	Ridge parameter in the diagonal of \bold{X}'\bold{X}

Value

A vector of length nmis=sum(!ry) with imputed values.

Examples

## Not run: 
#############################################################################
# EXAMPLE 1: Sequential hierarchical imputation for data.ma05 dataset
#############################################################################

data(data.ma05)
dat <- data.ma05

# define predictor matrix
predM <- mice::make.predictorMatrix(data=dat)
# exclude student IDs
predM[, "idstud"] <- 0
# define idclass as the cluster variable (type=-2)
predM[, "idclass" ] <- -2

# initialize with norm method
impMethod <- mice::make.method(data=dat)
names(impMethod) <- names( imp0$method )
impMethod[ c("idstud","idclass")] <- ""

#*****
# STUDENT LEVEL (Level 1)

# Use a random slope model for Dscore and Mscore as the imputation method.
# Here, variance homogeneity of residuals is assumed (contrary to
# the 2l.norm imputation method in the mice package).
impMethod[ c("Dscore", "Mscore") ] <- "2l.pan"
predM[ c("Dscore","Mscore"), "misei" ] <- 2    # random slopes on 'misei'
predM[, "idclass" ] <- -2

# For imputing 'manote' and 'denote' use contextual effects (i.e. cluszer means)
# of variables 'misei' and 'migrant'
impMethod[ c("denote", "manote") ] <- "2l.contextual.pmm"
predM[ c("denote", "manote"), c("misei","migrant")] <- 2

# Use no cluster variable 'idclass' for imputation of 'misei'
impMethod[ "misei"] <- "norm"
predM[ "misei", "idclass"] <- 0 # use no multilevel imputation model

# Variable migrant: contextual effects of Dscore and misei
impMethod[ "migrant"] <- "2l.contextual.pmm"
predM[ "migrant", c("Dscore",  "misei" ) ] <- 2
predM[ "migrant", "idclass" ] <- -2

#****
# CLASS LEVEL (Level 2)
# impute 'sprengel' and 'groesse' at the level of classes
impMethod[ "sprengel"] <- "2lonly.pmm"
impMethod[ "groesse"] <- "2lonly.norm"
predM[ c("sprengel","groesse"), "idclass" ] <- -2

# do imputation
imp <- mice::mice( dat, predictorMatrix=predM, m=3,  maxit=4,
           method=impMethod, paniter=100)
summary(imp)

#**** imputation model 2 with PLS dimension reduction

# define some interaction effects
interactions <- list( manote=c("migrant", "misei") )
# number of PLS factors (5 factors)
pls.facs <- list( manote=5 )

# do imputation
imp2 <- mice::mice( dat, predictorMatrix=predM, interactions=interactions,
            pls.facs=pls.facs, method=impMethod, paniter=100)
summary(imp2)

## End(Not run)

miceadds

Some Additional Multiple Imputation Functions, Especially for 'mice'

v3.11-6

GPL (>= 2)

Authors

Alexander Robitzsch [aut,cre] (<https://orcid.org/0000-0002-8226-3132>), Simon Grund [aut] (<https://orcid.org/0000-0002-1290-8986>), Thorsten Henke [ctb]

Initial release

2021-01-21 11:48:47