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miceFast

Maciej Nasinski

Check the miceFast website for more details

Fast imputations under the object-oriented programming paradigm.
Moreover there are offered a few functions built to work with popular R packages such as ‘data.table’ or ‘dplyr’. The biggest improvement in time performance could be achieve for a calculation where a grouping variable have to be used. A single evaluation of a quantitative model for the multiple imputations is another major enhancement. A new major improvement is one of the fastest predictive mean matching in the R world because of presorting and binary search.

Performance benchmarks (check performance_validity.R file at extdata).

Advanced Usage - Vignette

Installation

install.packages('miceFast')

# install.packages("devtools")
devtools::install_github("polkas/miceFast")

Recommended to download boosted BLAS library, even x100 faster:

Windows Users recommended to download MRO MKL: https://mran.microsoft.com/download
Linux users recommended to download Optimized BLAS (linear algebra) library: sudo apt-get install libopenblas-dev
Apple vecLib BLAS:

cd /Library/Frameworks/R.framework/Resources/lib
ln -sf /System/Library/Frameworks/Accelerate.framework/Frameworks/vecLib.framework/Versions/Current/libBLAS.dylib libRblas.dylib

Quick Implementation

library(miceFast)

set.seed(1234)
data(air_miss)

# plot NA structure
upset_NA(air_miss, 6)

naive_fill_NA(air_miss)

# Check out the vignette for an advance usage
# There is required a thorough examination

# Other packages - popular simple solutions
# Hmisc
data.frame(Map(function(x) Hmisc::impute(x, 'random'), air_miss))

#mice
mice::complete(mice::mice(air_miss, printFlag = FALSE))

Quick Reference Table

Function	Description
`new(miceFast)`	OOP instance with bunch of methods - check out vignette
`fill_NA()`	imputation - lda,lm_pred,lm_bayes,lm_noise
`fill_NA_N()`	multiple imputation - pmm,lm_bayes,lm_noise
`VIF()`	Variance inflation factor
`naive_fill_NA()`	auto imputations
`compare_imp()`	comparing imputations
`upset_NA()`	visualize NA structure - UpSetR::upset

Summing up, miceFast offer a relevant reduction of a calculations time for:

Linear Discriminant Analysis around (x5)
where a grouping variable have to be used (around x10 depending on data dimensions and number of groups and even more than x100 although compared to data.table only a few k faster or even the same) because of pre-sorting by grouping variable
multiple imputations is faster around x(a number of multiple imputations) because the core of a model is evaluated only ones.
Variance inflation factors (VIF) (x5) because the unnecessary linear regression is not evaluated - we need only inverse of X’X
Predictive mean matching (PMM) (x3) because of pre-sorting and binary search (mice algorithm was improved too).

Environment: R 4.2.1 Mac M1

If you are interested about the procedure of testing performance and validity check performance_validity.R file at the extdata folder.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.