reduce_felm_object_size: Reduce the size of an felm object

In ChandlerLutz/CLmisc: Chandler Lutz's Miscellaneous R functions

Reduce the size of an felm object

Description

felm_reduce_object_size reduces the size of an an felm object. This function is helpful for clearing up memory after running a regression or saving and felm object to the disk using saveRDS. Running felm_reduce_object_size, preserves using lfe::summary.felm, stargazer::stargazer, broom::tidy, and CLmisc::felm_broom_tidy.

Usage

reduce_felm_object_size(felm.object)

Arguments

felm.object

The object of type felm

Details

__NOTE__: The r-squared, etc, will not be preserved as this function deletes residuals to save space. Also, when calling broom::tidy or stargazer::stargazer, you'll get an error saying that a F-statistic can't be reported. This is extra output used by summary.felm. Please ignore this as it affects this rather un-useful F-statistic. You may also see other warnings or errors. These can be safely ignored as they often just produce non-useful summary stats

Examples

library(lfe); library(stargazer)

## Simulate data -- from ?lfe::felm

# Covariates
x <- rnorm(1000)
x2 <- rnorm(length(x))
# Individuals and firms
id <- factor(sample(20,length(x),replace=TRUE))
firm <- factor(sample(13,length(x),replace=TRUE))
# Effects for them
id.eff <- rnorm(nlevels(id))
firm.eff <- rnorm(nlevels(firm))
# Left hand side
u <- rnorm(length(x))
y <- x + 0.5*x2 + id.eff[id] + firm.eff[firm] + u

## Estimate the model and print the results
est <- felm(y ~ x + x2 | id + firm)

## Example with 'reverse causation' (IV regression)

# Q and W are instrumented by x3 and the factor x4.
x3 <- rnorm(length(x))
x4 <- sample(12,length(x),replace=TRUE)
Q <- 0.3*x3 + x + 0.2*x2 + id.eff[id] + 0.3*log(x4) - 0.3*y + rnorm(length(x),sd=0.3)
W <- 0.7*x3 - 2*x + 0.1*x2 - 0.7*id.eff[id] + 0.8*cos(x4) - 0.2*y+ rnorm(length(x),sd=0.6)
# Add them to the outcome variable
y <- y + Q + W

## Estimate the IV model and report robust SEs
ivest <- felm(y ~ x + x2 | id + firm | (Q|W ~ x3 + factor(x4)))

# Example with multiway clustering

# Create a large cluster group (500 clusters) and a small one (20 clusters)
cl1 <- factor(sample(rep(1:500, length.out=length(x))))
cl2 <- factor(sample(rep(1:20, length.out=length(x))))
# Function for adding clustered noise to our outcome variable 
cl_noise <- function(cl) {
 obs_per_cluster <- length(x)/nlevels(cl)
 unlist(replicate(nlevels(cl), rnorm(obs_per_cluster, mean=rnorm(1), sd=runif(1)), simplify=FALSE))
}
# New outcome variable
y_cl <- x + 0.5*x2 + id.eff[id] + firm.eff[firm] + cl_noise(cl1) + cl_noise(cl2)

## Estimate and print the model with cluster-robust SEs (default)
est_cl <- felm(y_cl ~ x + x2 | id + firm | 0 | cl1 + cl2)

est.small <- reduce_felm_object_size(est)
ivest.small <- reduce_felm_object_size(ivest)
est_cl.small <- reduce_felm_object_size(est_cl)

identical(felm_broom_tidy(est), felm_broom_tidy(est.small))
identical(felm_broom_tidy(ivest), felm_broom_tidy(ivest.small))
identical(felm_broom_tidy(est_cl), felm_broom_tidy(est_cl.small))

identical(stargazer(est, keep.stat = "N", se = list(est$rse)),
          stargazer(est.small, keep.stat = "N", se = list(est.small$rse)))

identical(stargazer(ivest, keep.stat = "N", se = list(ivest$rse)),
          stargazer(ivest.small, keep.stat = "N", se = list(ivest.small$rse)))

identical(stargazer(est_cl, keep.stat = "N"),
          stargazer(est_cl.small, keep.stat = "N"))

## -- Size Differences of examples
object.size(est) / object.size(est.small)
object.size(ivest) / object.size(ivest.small)
object.size(est_cl) / object.size(est_cl.small)

ChandlerLutz/CLmisc documentation built on Dec. 2, 2022, 12:40 p.m.