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 across models but is usually self-evident. If a model has several distinct types of components, you will need to specify which components to return.

Usage

# S3 method for class 'coeftest'
tidy(x, conf.int = FALSE, conf.level = 0.95, ...)

Arguments

x

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

conf.int

Logical indicating whether or not to include a confidence interval in the tidied output. Defaults to FALSE.

conf.level

The confidence level to use for the confidence interval if conf.int = TRUE. Must be strictly greater than 0 and less than 1. Defaults to 0.95, which corresponds to a 95 percent confidence interval.

...

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. Two exceptions here are:

tidy() methods will warn when supplied an exponentiate argument if it will be ignored.
augment() methods will warn when supplied a newdata argument if it will be ignored.

Value

A tibble::tibble() with columns:

conf.high: Upper bound on the confidence interval for the estimate.
conf.low: Lower bound on the confidence interval for the estimate.
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


# load libraries for models and data
library(lmtest)

m <- lm(dist ~ speed, data = cars)

coeftest(m)
#> 
#> t test of coefficients:
#> 
#>              Estimate Std. Error t value Pr(>|t|)    
#> (Intercept) -17.57909    6.75844 -2.6011  0.01232 *  
#> speed         3.93241    0.41551  9.4640 1.49e-12 ***
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> 
tidy(coeftest(m))
#> # A tibble: 2 × 5
#>   term        estimate std.error statistic  p.value
#>   <chr>          <dbl>     <dbl>     <dbl>    <dbl>
#> 1 (Intercept)   -17.6      6.76      -2.60 1.23e- 2
#> 2 speed           3.93     0.416      9.46 1.49e-12
tidy(coeftest(m, conf.int = TRUE))
#> # A tibble: 2 × 5
#>   term        estimate std.error statistic  p.value
#>   <chr>          <dbl>     <dbl>     <dbl>    <dbl>
#> 1 (Intercept)   -17.6      6.76      -2.60 1.23e- 2
#> 2 speed           3.93     0.416      9.46 1.49e-12

# a very common workflow is to combine lmtest::coeftest with alternate
# variance-covariance matrices via the sandwich package. The lmtest
# tidiers support this workflow too, enabling you to adjust the standard
# errors of your tidied models on the fly.
library(sandwich)

# "HC3" (default) robust SEs
tidy(coeftest(m, vcov = vcovHC))
#> # A tibble: 2 × 5
#>   term        estimate std.error statistic  p.value
#>   <chr>          <dbl>     <dbl>     <dbl>    <dbl>
#> 1 (Intercept)   -17.6      5.93      -2.96 4.72e- 3
#> 2 speed           3.93     0.428      9.20 3.64e-12

# "HC2" robust SEs
tidy(coeftest(m, vcov = vcovHC, type = "HC2"))
#> # A tibble: 2 × 5
#>   term        estimate std.error statistic  p.value
#>   <chr>          <dbl>     <dbl>     <dbl>    <dbl>
#> 1 (Intercept)   -17.6      5.73      -3.07 3.55e- 3
#> 2 speed           3.93     0.413      9.53 1.21e-12

# N-W HAC robust SEs
tidy(coeftest(m, vcov = NeweyWest))
#> # A tibble: 2 × 5
#>   term        estimate std.error statistic       p.value
#>   <chr>          <dbl>     <dbl>     <dbl>         <dbl>
#> 1 (Intercept)   -17.6      7.02      -2.50 0.0157       
#> 2 speed           3.93     0.551      7.14 0.00000000453

# the columns of the returned tibble for glance.coeftest() will vary
# depending on whether the coeftest object retains the underlying model.
# Users can control this with the "save = TRUE" argument of coeftest().
glance(coeftest(m))
#> # A tibble: 1 × 4
#>   logLik     AIC   BIC  nobs
#>   <chr>    <dbl> <dbl> <int>
#> 1 -206.578  419.  425.    50
glance(coeftest(m, save = TRUE))
#> # A tibble: 1 × 12
#>   r.squared adj.r.squared sigma statistic  p.value    df logLik   AIC
#>       <dbl>         <dbl> <dbl>     <dbl>    <dbl> <dbl>  <dbl> <dbl>
#> 1     0.651         0.644  15.4      89.6 1.49e-12     1  -207.  419.
#> # ℹ 4 more variables: BIC <dbl>, deviance <dbl>, df.residual <int>,
#> #   nobs <int>

Usage

Arguments

See also

Value

Examples