Imputation by Predictive Mean Matching (in miceadds)
This function imputes values by predictive mean matching like
the mice::mice.impute.pmm
method in the mice package.
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), ...)
y |
Incomplete data vector of length |
ry |
Vector of missing data pattern ( |
x |
Matrix ( |
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 |
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.
A vector of length nmis=sum(!ry) with imputed values.
See data.largescale and data.smallscale
for speed comparisons of different functions for predictive mean
matching.
## 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
## End(Not run)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.