misl: MISL: Multiple Imputation by Super Learning (v2.0)

In misl: Multiple Imputation by Super Learning

MISL: Multiple Imputation by Super Learning (v2.0)

Description

Imputes missing values using multiple imputation by super learning.

Usage

misl(
  dataset,
  m = 5,
  maxit = 5,
  seed = NA,
  con_method = c("glm", "rand_forest", "boost_tree"),
  bin_method = c("glm", "rand_forest", "boost_tree"),
  cat_method = c("rand_forest", "boost_tree"),
  ord_method = c("polr", "rand_forest", "boost_tree"),
  cv_folds = 5,
  ignore_predictors = NA,
  quiet = TRUE
)

Arguments

dataset	A dataframe or matrix containing the incomplete data. Missing values are represented with NA.
m	The number of multiply imputed datasets to create. Default 5.
maxit	The number of iterations per imputed dataset. Default 5.
seed	Integer seed for reproducibility, or NA to skip. Default NA.
con_method	Character vector of learner IDs, a list of parsnip model specs, or a mixed list of both, for continuous columns. Default c("glm", "rand_forest", "boost_tree").
bin_method	Character vector of learner IDs, a list of parsnip model specs, or a mixed list of both, for binary columns (values must be 0/1/NA or a two-level factor). Default c("glm", "rand_forest", "boost_tree").
cat_method	Character vector of learner IDs, a list of parsnip model specs, or a mixed list of both, for unordered categorical columns. Default c("rand_forest", "boost_tree").
ord_method	Character vector of learner IDs, a list of parsnip model specs, or a mixed list of both, for ordered categorical columns. Default c("polr", "rand_forest", "boost_tree").
cv_folds	Integer number of cross-validation folds used when stacking multiple learners. Reducing this (e.g. to 3) speeds up computation at a small cost to ensemble accuracy. Default 5. Ignored when only a single learner is supplied.
ignore_predictors	Character vector of column names to exclude as predictors. Default NA.
quiet	Suppress console progress messages. Default TRUE.

Details

Built-in named learners (see list_learners()):

• "glm" - base R (logistic for binary, linear for continuous)

• "rand_forest" - ranger

• "boost_tree" - xgboost

• "mars" - earth

• "multinom_reg" - nnet (unordered categorical only)

• "polr" - MASS (ordered categorical only)

Any parsnip-compatible model spec can also be passed directly via the *_method arguments. Named strings and parsnip specs can be mixed in the same list:

library(parsnip)
misl(data,
  con_method = list(
    "glm",
    rand_forest(trees = 500) |> set_engine("ranger")
  )
)

The mode (regression vs classification) is always enforced by misl regardless of what is set on the spec.

Value

A list of m named lists, each with:

datasets

A fully imputed tibble.

trace

A long-format tibble of mean/sd trace statistics per iteration, for convergence inspection.

Parallelism

Imputation across the m datasets is parallelised via future.apply. To enable parallel execution, set a future plan before calling misl():

library(future)
plan(multisession, workers = 4)
result <- misl(data, m = 5)
plan(sequential)

Examples

# Using named learners (same as v1.0)
set.seed(1)
n <- 100
demo_data <- data.frame(x1 = rnorm(n), x2 = rnorm(n), y = rnorm(n))
demo_data[sample(n, 10), "y"] <- NA
misl_imp <- misl(demo_data, m = 2, maxit = 2, con_method = "glm")

# Using a custom parsnip spec
## Not run: 
library(parsnip)
misl_imp <- misl(
  demo_data, m = 2, maxit = 2,
  con_method = list(
    "glm",
    rand_forest(trees = 500) |> set_engine("ranger")
  )
)

## End(Not run)

misl documentation built on April 8, 2026, 9:07 a.m.