Extract model data for the texreg package

Prepares an lm_robust or iv_robust fit for texreg. Largely a clone of texreg's own extract.lm method.

Usage

extract.lm_robust(
  model,
  include.ci = TRUE,
  include.rsquared = TRUE,
  include.adjrs = TRUE,
  include.nobs = TRUE,
  include.fstatistic = FALSE,
  include.rmse = TRUE,
  include.nclusts = TRUE,
  ...
)

extract.iv_robust(
  model,
  include.ci = TRUE,
  include.rsquared = TRUE,
  include.adjrs = TRUE,
  include.nobs = TRUE,
  include.fstatistic = FALSE,
  include.rmse = TRUE,
  include.nclusts = TRUE,
  ...
)

Arguments

model

An lm_robust or iv_robust fit.

include.ci, include.rsquared, include.adjrs, include.nobs

Logical.

include.fstatistic, include.rmse, include.nclusts

Logical.

...

(optional) Ignored.

Value

A texreg object.

Details

These are exported as plain functions rather than registered with S3method() because that is how texreg finds them: it looks up extract.<class> by name in the package namespace rather than dispatching on a generic it owns. Registering them the usual way would leave texreg unable to see them.

texreg is the only consumer. Table building through modelsummary needs nothing here, since it reads tidy() and glance() and so already works on every estimator in this package.

Examples

set.seed(60)
dat <- data.frame(x = rnorm(50), z = rep(0:1, 25))
dat$y <- dat$x + 0.4 * dat$z + rnorm(50)
fit <- lm_robust(y ~ x + z, data = dat)

if (requireNamespace("texreg", quietly = TRUE)) {
  texreg::screenreg(fit)
}
#> 
#> ==========================
#>              Model 1      
#> --------------------------
#> (Intercept)    0.32       
#>              [-0.03; 0.67]
#> x              0.98 *     
#>              [ 0.68; 1.28]
#> z              0.04       
#>              [-0.49; 0.56]
#> --------------------------
#> R^2            0.55       
#> Adj. R^2       0.53       
#> Num. obs.     50          
#> RMSE           0.92       
#> ==========================
#> * 0 outside the confidence interval.