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