Package:
simputation
Category:
Single Imputation, Meta-Package
Use-Cases:
Use imputation algortihms of multiple packages via one interface.
Popularity:
Description:
Easy to use interfaces to a number of imputation methods that fit in the not-a-pipe operator of the ‘magrittr’ package.
Last update:
Algorithms:
- impute_cart Decision Tree Imputation
- impute_const Impute by variable derivation
- impute_em Multivariate, model-based imputation
- impute_en (Robust) Linear Regression Imputation
- impute_hotdeck Hot deck imputation
- impute_knn Hot deck imputation
- impute_lm (Robust) Linear Regression Imputation
- impute_median Impute (group-wise) medians
- impute_mf Multivariate, model-based imputation
- impute_multivariate Multivariate, model-based imputation
- impute_pmm Hot deck imputation
- impute_proxy Impute by variable derivation
- impute_rf Decision Tree Imputation
- impute_rhd Hot deck imputation
- impute_rlm (Robust) Linear Regression Imputation
- impute_shd Hot deck imputation
Datasets:
none
Further Information:
Announcing the simputation package: make imputation simple (https://www.r-bloggers.com/announcing-the-simputation-package-make-imputation-simple/)
Easy imputation with the simputation package, useR! 2017 (https://www.slideshare.net/MarkVanDerLoo/user2017markvanderloo)
http://analyticsyatra.com/RNotebooks/Impute_Missing_Data.nb.html
“An introduction to imputation”, uRos 2018 (http://r-project.ro/conference2018/presentations/simputation_presentation.pdf)
Input:
data.frame
Example:
library("simputation")
# create dataset with missing data for testing purposes
dat <- iris
dat[1:3,1] <- dat[3:7,2] <- dat[8:10,5] <- NA
print("before imputation")
head(dat,10)
# Impute variables Sepal.Length + Sepal.Width
da5 <- impute_rlm(dat, Sepal.Length + Sepal.Width ~ Petal.Length + Species)
print("after imputation")
head(da5)
Here you can have a interactive look at the example:
https://rdrr.io/snippets/embedding/