Build lm_robust object from lm fit

commarobust(model, se_type = NULL, clusters = NULL, ci = TRUE, alpha = 0.05)

Arguments

model

an lm model object

se_type

The sort of standard error sought. If clusters is not specified the options are "HC0", "HC1" (or "stata", the equivalent), "HC2" (default), "HC3", or "classical". If clusters is specified the options are "CR0", "CR2" (default), or "stata". Can also specify "none", which may speed up estimation of the coefficients.

clusters

A vector corresponding to the clusters in the data.

ci

logical. Whether to compute and return p-values and confidence intervals, TRUE by default.

alpha

The significance level, 0.05 by default.

Value

an lm_robust object.

Examples

lmo <- lm(mpg ~ hp, data = mtcars)

# Default HC2
commarobust(lmo)
#>                Estimate Std. Error   t value    Pr(>|t|)    CI Lower
#> (Intercept) 30.09886054 2.19301194 13.724896 1.81366e-14 25.62013267
#> hp          -0.06822828 0.01471473 -4.636732 6.48546e-05 -0.09827977
#>                CI Upper DF
#> (Intercept) 34.57758841 30
#> hp          -0.03817678 30

commarobust(lmo, se_type = "HC3")
#>                Estimate Std. Error   t value     Pr(>|t|)   CI Lower
#> (Intercept) 30.09886054 2.41006671 12.488808 2.044330e-13 25.1768477
#> hp          -0.06822828 0.01660193 -4.109659 2.822529e-04 -0.1021339
#>                CI Upper DF
#> (Intercept) 35.02087341 30
#> hp          -0.03432261 30

commarobust(lmo, se_type = "stata", clusters = mtcars$carb)
#>                Estimate Std. Error   t value     Pr(>|t|)   CI Lower
#> (Intercept) 30.09886054 2.15609050 13.959924 3.390807e-05 24.5564535
#> hp          -0.06822828 0.01404901 -4.856449 4.647551e-03 -0.1043424
#>                CI Upper DF
#> (Intercept) 35.64126761  5
#> hp          -0.03211416  5