Proof of concept: reggie and texreg

[vincentarelbundock]

This morning, another student came to my office asking how to produce tables with clustered standard errors. So here I am again.

I just wanted to point out a neat feature that I contributed to texreg a little while ago: When the package doesn't recognize the model type, it tries to extract info using broom. Thanks to that, we can easily present side-by-side estimates from reggie by creating a couple very simple broom helpers:

library(reggie)
library(texreg)
library(broom)

# Broom Helper Functions
tidy.reg <- function(x, ...) {
    ret <- data.frame('term' = row.names(x$coefficients),
                      'estimate' = x$coefficients[, 1], 
                      'std.error' = x$coefficients[, 2], 
                      'statistic' = x$coefficients[, 3], 
                      'p.value' = x$coefficients[, 4], 
                      row.names = NULL,
  stringsAsFactors = FALSE)
    return(ret)
}
glance.reg <- function(x, ...) {
    ret <- glance(x$model)
    return(ret)
}

# Simulated Data
dat <- data.frame('x' = rnorm(1000),
                  'z' = sample(letters, 1000, replace = TRUE))
dat$y <- ifelse(dat$x + rnorm(1000) > 0, 1, 0)

# Fitting models
models <- list()
models[['GLM']] <- glm(y ~ x, data = dat, family = binomial())
models[['Cluster']] <- reg(y ~ x, data = dat, family = binomial(), vcov_cluster = ~ z)
models[['HC1']] <- reg(y ~ x, data = dat, family = binomial(), vcov_type = 'HC1')

# Summary
screenreg(models, digits = 4)

============================================================
                 GLM            Cluster        HC1 
------------------------------------------------------------
(Intercept)        -0.1355        -0.1355        -0.1355
                   (0.0774)       (0.0762)       (0.0774)
x                   1.6487 ***     1.6487 ***     1.6487 *** 
                   (0.1099)       (0.1060)       (0.1138)
------------------------------------------------------------
AIC              1013.3481      1013.3481      1013.3481
BIC              1023.1637      1023.1637      1023.1637
Log Likelihood   -504.6741      -504.6741      -504.6741
Deviance         1009.3481      1009.3481      1009.3481
Num. obs.        1000
Deviance (Null)                 1382.6922      1382.6922
df.null                          999            999 
DF Resid.                        998            998 
============================================================
*** p < 0.001, ** p < 0.01, * p < 0.05

Not perfect, but pretty close...