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GitHub - markvanderloo/simputation: Making imputation easy

An R package to make imputation simple. Currently supported methods include

• Model based (optionally add [non-]parametric random residual)
  • linear regression
  • robust linear regression (M-estimation)
  • ridge/elasticnet/lasso regression (from version >= 0.2.1)
  • CART models
  • Random forest
• Model based, multivariate
  • Imputation based on EM-estimated parameters (from version >= 0.2.1)
  • missForest (from version >= 0.2.1)
• Donor imputation (including various donor pool specifications)
  • k-nearest neigbour (based on gower's distance)
  • sequential hotdeck (LOCF, NOCB)
  • random hotdeck
  • Predictive mean matching
• Other
  • (groupwise) median imputation (optional random residual)
  • Proxy imputation (copy from other variable)

To install simputation and all packages needed to support various imputation models do the following.

install.packages("simputation", dependencies=TRUE)

To install the development version.

git clone https://github.com/markvanderloo/simputation
make install

Create some data suffering from missings

library(simputation) # current package

dat <- iris
# empty a few fields
dat[1:3,1] <- dat[3:7,2] <- dat[8:10,5] <- NA
head(dat,10)

Now impute Sepal.Length and Sepal.Width by regression on Petal.Length and Species, and impute Species using a CART model, that uses all other variables (including the imputed variables in this case).

dat |>
  impute_lm(Sepal.Length + Sepal.Width ~ Petal.Length + Species) |>
  impute_cart(Species ~ .) |> # use all variables except 'Species' as predictor
  head(10)

• The introductory vignette
• slides from my useR2017 talk.

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