mice.impute.pmm3: Imputation by Predictive Mean Matching (in miceadds)

Description

This function imputes values by predictive mean matching like the mice::mice.impute.pmm method in the mice package.

Usage

mice.impute.pmm3(y, ry, x, donors=3, noise=10^5, ridge=10^(-5), ...)
mice.impute.pmm4(y, ry, x, donors=3, noise=10^5, ridge=10^(-5), ...)
mice.impute.pmm5(y, ry, x, donors=3, noise=10^5, ridge=10^(-5), ...)
mice.impute.pmm6(y, ry, x, donors=3, noise=10^5, ridge=10^(-5), ...)

Arguments

Incomplete data vector of length n

Vector of missing data pattern (FALSE -- missing, TRUE -- observed)

Matrix (n x p) of complete covariates.

donors

Number of donors used for imputation

noise

Numerical value to break ties

ridge

Ridge parameter in the diagonal of \( \bold{X}'\bold{X}\)

…

Further arguments to be passed

Value

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

Details

The imputation method pmm3 imitates mice::mice.impute.pmm imputation method in mice.

The imputation method pmm4 ignores ties in predicted \(y\) values. With many predictors, this does not probably implies any substantial problem.

The imputation method pmm5 suffers from the same problem. Contrary to the other PMM methods, it searches \(D\) donors (specified by donors) smaller than the predicted value and \(D\) donors larger than the predicted value and randomly samples a value from this set of \(2 \cdot D\) donors.

The imputation method pmm6 is just the Rcpp implementation of pmm5.

Examples

Run this code

# NOT RUN {
#############################################################################
# SIMULATED EXAMPLE 1: Two variables x and y with missing y
#############################################################################
set.seed(1413)

rho <- .6   # correlation between x and y
N <- 6800    # number of cases
x <- stats::rnorm(N)
My <- .35   # mean of y
y.com <- y <- My + rho * x + stats::rnorm(N, sd=sqrt( 1 - rho^2 ) )

# create missingness on y depending on rho.MAR parameter
rho.mar <- .4    # correlation response tendency z and x
missrate <- .25  # missing response rate
# simulate response tendency z and missings on y
z <- rho.mar * x + stats::rnorm(N, sd=sqrt( 1 - rho.mar^2 ) )
y[ z < stats::qnorm( missrate ) ] <- NA
dat <- data.frame(x, y )

# mice imputation
impmethod <- rep("pmm", 2 )
names(impmethod) <- colnames(dat)

# pmm (in mice)
imp1 <- mice::mice( as.matrix(dat), m=1, maxit=1, method=impmethod)
# pmm3 (in miceadds)
imp3 <- mice::mice( as.matrix(dat), m=1, maxit=1,
           method=gsub("pmm","pmm3",impmethod)  )
# pmm4 (in miceadds)
imp4 <- mice::mice( as.matrix(dat), m=1, maxit=1,
           method=gsub("pmm","pmm4",impmethod)  )
# pmm5 (in miceadds)
imp5 <- mice::mice( as.matrix(dat), m=1, maxit=1,
           method=gsub("pmm","pmm5",impmethod)  )
# pmm6 (in miceadds)
imp6 <- mice::mice( as.matrix(dat), m=1, maxit=1,
           method=gsub("pmm","pmm6",impmethod)  )

dat.imp1 <- mice::complete( imp1, 1 )
dat.imp3 <- mice::complete( imp3, 1 )
dat.imp4 <- mice::complete( imp4, 1 )
dat.imp5 <- mice::complete( imp5, 1 )
dat.imp6 <- mice::complete( imp6, 1 )

dfr <- NULL
# means
dfr <- rbind( dfr, c( mean( y.com ), mean( y, na.rm=TRUE ), mean( dat.imp1$y),
    mean( dat.imp3$y), mean( dat.imp4$y), mean( dat.imp5$y),  mean( dat.imp6$y)  ) )
# SD
dfr <- rbind( dfr, c( stats::sd( y.com ), stats::sd( y, na.rm=TRUE ),
      stats::sd( dat.imp1$y), stats::sd( dat.imp3$y), stats::sd( dat.imp4$y),
      stats::sd( dat.imp5$y), stats::sd( dat.imp6$y) ) )
# correlations
dfr <- rbind( dfr, c( stats::cor( x,y.com ),
    stats::cor( x[ ! is.na(y) ], y[ ! is.na(y) ] ),
    stats::cor( dat.imp1$x, dat.imp1$y), stats::cor( dat.imp3$x, dat.imp3$y),
    stats::cor( dat.imp4$x, dat.imp4$y), stats::cor( dat.imp5$x, dat.imp5$y),
    stats::cor( dat.imp6$x, dat.imp6$y)
        ) )
rownames(dfr) <- c("M_y", "SD_y", "cor_xy" )
colnames(dfr) <- c("compl", "ld", "pmm", "pmm3", "pmm4", "pmm5","pmm6")
##           compl     ld    pmm   pmm3   pmm4   pmm5   pmm6
##   M_y    0.3306 0.4282 0.3314 0.3228 0.3223 0.3264 0.3310
##   SD_y   0.9910 0.9801 0.9873 0.9887 0.9891 0.9882 0.9877
##   cor_xy 0.6057 0.5950 0.6072 0.6021 0.6100 0.6057 0.6069
# }