clean_fit_glm: Fit a stats::glm without carying back large structures.

In wrapr: Wrap R Tools for Debugging and Parametric Programming

Fit a stats::glm 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_glm(
  outcome,
  variables,
  data,
  ...,
  family,
  intercept = TRUE,
  outcome_target = NULL,
  outcome_comparator = "==",
  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
family	passed to stats::glm()
intercept	logical, if TRUE allow an intercept term.
outcome_target	scalar, if not NULL write outcome==outcome_target in formula.
outcome_comparator	one of "==", "!=", ">=", "<=", ">", "<", only use of outcome_target is not NULL.
weights	passed to stats::glm()
env	environment to work in.

Value

list(model=model, summary=summary)

Examples

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_glm <- clean_fit_glm("yC", c("x1", "x2"),
                         mk_data_example(1),
                         family = binomial)
length(serialize(res_glm$model, NULL))

res_glm <- clean_fit_glm("yC", c("x1", "x2"),
                         mk_data_example(10000),
                         family = binomial)
length(serialize(res_glm$model, NULL))

predict(res_glm$model,
        newdata = mk_data_example(1),
        type = "response")

wrapr documentation built on Aug. 20, 2023, 1:08 a.m.