Tidy a(n) coeftest object

Tidy summarizes information about the components of a model. A model component might be a single term in a regression, a single hypothesis, a cluster, or a class. Exactly what tidy considers to be a model component varies cross models but is usually self-evident. If a model has several distinct types of components, you will need to specify which components to return.

# S3 method for coeftest
tidy(x, ...)

Arguments

x

A coeftest object returned from lmtest::coeftest().

...

Additional arguments. Not used. Needed to match generic signature only. Cautionary note: Misspelled arguments will be absorbed in ..., where they will be ignored. If the misspelled argument has a default value, the default value will be used. For example, if you pass conf.lvel = 0.9, all computation will proceed using conf.level = 0.95. Additionally, if you pass newdata = my_tibble to an augment() method that does not accept a newdata argument, it will use the default value for the data argument.

See also

tidy(), lmtest::coeftest()

Value

A tibble::tibble() with columns:

estimate

The estimated value of the regression term.

p.value

The two-sided p-value associated with the observed statistic.

statistic

The value of a T-statistic to use in a hypothesis that the regression term is non-zero.

std.error

The standard error of the regression term.

term

The name of the regression term.

Examples

library(lmtest)

data(Mandible)
fm <- lm(length ~ age, data = Mandible, subset = (age <= 28))

lmtest::coeftest(fm)
#> 
#> t test of coefficients:
#> 
#>               Estimate Std. Error t value  Pr(>|t|)    
#> (Intercept) -11.953366   0.976227 -12.245 < 2.2e-16 ***
#> age           1.772730   0.047704  37.161 < 2.2e-16 ***
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> 
tidy(coeftest(fm))
#> # A tibble: 2 x 5
#>   term        estimate std.error statistic  p.value
#>   <chr>          <dbl>     <dbl>     <dbl>    <dbl>
#> 1 (Intercept)   -12.0     0.976      -12.2 1.39e-24
#> 2 age             1.77    0.0477      37.2 2.15e-79