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

Source: R/clean_fit.R

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

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


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))

#> [1] 1506

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

#> [1] 1506

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

#>   1   2   3   4 
#> 1.5 1.5 3.0 4.0