mice.impute.rlm: Imputation of a Linear Model by Bayesian Bootstrap

In miceadds: Some Additional Multiple Imputation Functions, Especially for 'mice'

Imputation of a Linear Model by Bayesian Bootstrap

Description

These functions impute from linear models using the functions stats::lm, MASS::rlm or MASS::lqs. The method mice.impute.lm_fun allows the definition of a general linear regression fitting function for which the methods predict and residuals are defined.

Parameters of the model are estimated by Bayesian bootstrap. Predicted values are computed and residuals are randomly drawn from the empirical distribution of residuals of observed data.

Usage

mice.impute.lm(y, ry, x, wy=NULL, lm_args=NULL, trafo=NULL, antitrafo=NULL, ...)

mice.impute.rlm(y, ry, x, wy=NULL, lm_args=NULL, trafo=NULL, antitrafo=NULL, ...)

mice.impute.lqs(y, ry, x, wy=NULL, lm_args=NULL, trafo=NULL, antitrafo=NULL, ...)

mice.impute.lm_fun(y, ry, x, wy=NULL, lm_args=NULL, lm_fun="lm", trafo=NULL,
               antitrafo=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.
wy	Vector of logicals indicating which entries should be imputed
lm_args	List of arguments for stats::lm, MASS::rlm, MASS::lqs or a user-defined function.
lm_fun	Linear regression fitting function, e.g. stats::lm for which S3 methods predict and residuals are defined.
trafo	Optional function for transforming the outcome values
antitrafo	Optional function which is the inverse function of trafo
...	Further arguments to be passed

Value

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

Examples

## Not run: 
#############################################################################
# EXAMPLE 1: Some toy example illustrating the methods
#############################################################################

library(MASS)
library(mice)

#-- simulate data
set.seed(98)
N <- 1000
x <- stats::rnorm(N)
z <- 0.5*x + stats::rnorm(N, sd=.7)
y <- stats::rnorm(N, mean=.3*x - .2*z, sd=1 )
dat <- data.frame(x,z,y)
dat[ seq(1,N,3), c("x","y") ] <- NA
dat[ seq(1,N,4), "z" ] <- NA

#-- define imputation methods
imp <- mice::mice(dat, maxit=0)
method <- imp$method
method["x"] <- "rlm"
method["z"] <- "lm"
method["y"] <- "lqs"

#-- impute data
imp <- mice::mice(dat, method=method)
summary(imp)

#--- example using transformations
dat1$x <- exp(dat1$x)
dat1$z <- stats::plogis(dat1$z)

trafo <- list(x=log, z=stats::qlogis)
antitrafo <- list(x=exp, z=stats::plogis)

#- impute with transformations
imp2 <- mice::mice(dat1, method=method, m=1, maxit=3, trafo=trafo, antitrafo=antitrafo)
print(imp2)

## End(Not run)

miceadds documentation built on Jan. 7, 2023, 1:09 a.m.