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).
install.packages('miceFast')
or
# install.packages("devtools")
devtools::install_github("polkas/miceFast")
Recommended to download boosted BLAS library, even x100 faster:
sudo apt-get install libopenblas-dev
cd /Library/Frameworks/R.framework/Resources/lib
ln -sf /System/Library/Frameworks/Accelerate.framework/Frameworks/vecLib.framework/Versions/Current/libBLAS.dylib libRblas.dylib
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:
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.
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