Maciej Nasinski pkgdown: https://polkas.github.io/miceFast/index.html
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("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 vigniette 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 = F))
Quick Reference Table
| Function | Description |
new(miceFast) | OOP instance with bunch of methods - check vigniette |
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:
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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