step_with_na {guideR} | R Documentation |
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, ...)
## Default S3 method:
step_with_na(model, ..., full_data = eval(model$call$data))
## S3 method for class 'svyglm'
step_with_na(model, ..., design)
Arguments
model |
A model object. |
... |
Additional parameters passed to |
full_data |
Full data frame used for the model, including missing data. |
design |
Survey design previously passed to |
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.
Value
The stepwise-selected model.
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
## 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