lm_strip.R

strip_glm <- function(cm) {
  cm$y = c()
  cm$model = c()
  
  cm$residuals = c()
  cm$fitted.values = c()
  cm$effects = c()
  cm$qr$qr = c()  
  cm$linear.predictors = c()
  cm$weights = c()
  cm$prior.weights = c()
  cm$data = c()

  
  cm$family$variance = c()
  cm$family$dev.resids = c()
  cm$family$aic = c()
  cm$family$validmu = c()
  cm$family$simulate = c()
  attr(cm$terms,".Environment") = c()
  attr(cm$formula,".Environment") = c()
  
  cm
}

gen_fake_data <- function(N) {
  data.frame(col1 = runif(N),
             col2 = runif(N),
             col3 = runif(N),
             resp = rnorm(N))
}

test_df <- gen_fake_data(1e5)

g <- glm(resp ~ col1 + col2 + col3, data = test_df)

object.size(g)
# 55255600 bytes
object.size(strip_glm(g))
# 22104 bytes

g <- lm(resp ~ col1 + col2 + col3, data = test_df)

object.size(g)
# 26417288 bytes
object.size(strip_glm(g))
# 8832 bytes