wrapr (version 2.0.2)

clean_fit_lm: Fit a stats::lm without carying back large structures.

Description

Please see https://win-vector.com/2014/05/30/trimming-the-fat-from-glm-models-in-r/ for discussion.

Usage

clean_fit_lm(
  outcome,
  variables,
  data,
  ...,
  intercept = TRUE,
  weights = NULL,
  env = baseenv()
)

Arguments

outcome

character, name of outcome column.

variables

character, names of varaible columns.

data

data.frame, training data.

...

not used, force later arguments to be used by name

intercept

logical, if TRUE allow an intercept term.

weights

passed to stats::glm()

env

environment to work in.

Value

list(model=model, summary=summary)

Examples

# NOT RUN {
mk_data_example <- function(k) {
  data.frame(
    x1 = rep(c("a", "a", "b", "b"), k),
    x2 = rep(c(0, 0, 0, 1), k),
    y = rep(1:4, k),
    yC = rep(c(FALSE, TRUE, TRUE, TRUE), k),
    stringsAsFactors = FALSE)
}

res_lm <- clean_fit_lm("y", c("x1", "x2"),
                       mk_data_example(1))
length(serialize(res_lm$model, NULL))

res_lm <- clean_fit_lm("y", c("x1", "x2"),
                       mk_data_example(10000))
length(serialize(res_lm$model, NULL))

predict(res_lm$model,
        newdata = mk_data_example(1))

# }