guideR (version 0.4.0)

step_with_na: Apply step(), taking into account missing values

Description

When your data contains missing values, concerned observations are removed from a model. However, then at a later stage, you try to apply a descending stepwise approach to reduce your model by minimization of AIC, you may encounter an error because the number of rows has changed.

Usage

step_with_na(model, ...)
# S3 method for default
step_with_na(model, ..., full_data = eval(model$call$data))
# S3 method for svyglm
step_with_na(model, ..., design)

Value

The stepwise-selected model.

Arguments

model

A model object.

...

Additional parameters passed to stats::step().

full_data

Full data frame used for the model, including missing data.

design

Survey design previously passed to survey::svyglm().

Details

step_with_na() applies the following strategy:

• recomputes the models using only complete cases;

• applies stats::step();

• recomputes the reduced model using the full original dataset.

step_with_na() has been tested with stats::lm(), stats::glm(), nnet::multinom(), survey::svyglm() and survival::coxph(). It may be working with other types of models, but with no warranty.

In some cases, it may be necessary to provide the full dataset initially used to estimate the model.

step_with_na() may not work inside other functions. In that case, you may try to pass full_data to the function.

Examples

set.seed(42)
d <- titanic |>
  dplyr::mutate(
    Group = sample(
      c("a", "b", NA),
      dplyr::n(),
      replace = TRUE
    )
  )
mod <- glm(as.factor(Survived) ~ ., data = d, family = binomial())
# step(mod) should produce an error
mod2 <- step_with_na(mod, full_data = d)
mod2

# \donttest{
## WITH SURVEY ---------------------------------------

library(survey)
ds <- d |>
  dplyr::mutate(Survived = as.factor(Survived)) |>
  srvyr::as_survey()
mods <- survey::svyglm(
  Survived ~ Class + Group + Sex,
  design = ds,
  family = quasibinomial()
)
mod2s <- step_with_na(mods, design = ds)
mod2s
# }