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

#############################################################################
# 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 <- rnorm(N)
My <- .35   # mean of y
y.com <- y <- My + rho * x + 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 + rnorm(N , sd = sqrt( 1 - rho.mar^2 ) )
y[ z < qnorm( missrate ) ] <- NA
dat <- data.frame(x , y )

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

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

dat.imp1 <- complete( imp1 , 1 )
dat.imp3 <- complete( imp3 , 1 )
dat.imp4 <- complete( imp4 , 1 )
dat.imp5 <- complete( imp5 , 1 )
dat.imp6 <- 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$y)  ) )
# SD
dfr <- rbind( dfr , c( sd( y.com ) , sd( y , na.rm=TRUE ) , sd( dat.imp1$y)  ,
    sd( dat.imp3$y) , sd( dat.imp4$y)  , sd( dat.imp5$y)  , sd( dat.imp6$y) ) )
# correlations
dfr <- rbind( dfr , c( cor( x,y.com ) , cor( x[ ! is.na(y) ] , y[ ! is.na(y) ] ) , 
    cor( dat.imp1$x , dat.imp1$y) , cor( dat.imp3$x , dat.imp3$y) , 
    cor( dat.imp4$x , dat.imp4$y) , cor( dat.imp5$x , dat.imp5$y)  , 
    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