Glance accepts a model object and returns a tibble::tibble() with exactly one row of model summaries. The summaries are typically goodness of fit measures, p-values for hypothesis tests on residuals, or model convergence information.

Glance never returns information from the original call to the modeling function. This includes the name of the modeling function or any arguments passed to the modeling function.

Glance does not calculate summary measures. Rather, it farms out these computations to appropriate methods and gathers the results together. Sometimes a goodness of fit measure will be undefined. In these cases the measure will be reported as NA.

Glance returns the same number of columns regardless of whether the model matrix is rank-deficient or not. If so, entries in columns that no longer have a well-defined value are filled in with an NA of the appropriate type.

# S3 method for coeftest
glance(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.

## Note

Because of the way that lmtest::coeftest() retains information about the underlying model object, the returned columns for glance.coeftest() will vary depending on the arguments. Specifically, four columns are returned regardless: "Loglik", "AIC", "BIC", and "nobs". Users can obtain additional columns (e.g. "r.squared", "df") by invoking the "save = TRUE" argument as part of lmtest::coeftest(). See examples.

As an aside, goodness-of-fit measures such as R-squared are unaffected by the presence of heteroskedasticity. For further discussion see, e.g. chapter 8.1 of Wooldridge (2016).

## References

Wooldridge, Jeffrey M. (2016) Introductory econometrics: A modern approach. (6th edition). Nelson Education.

glance(), lmtest::coeftest()

## Value

A tibble::tibble() with exactly one row and columns:

Adjusted R squared statistic, which is like the R squared statistic except taking degrees of freedom into account.

AIC

Akaike's Information Criterion for the model.

BIC

Bayesian Information Criterion for the model.

deviance

Deviance of the model.

df

Degrees of freedom used by the model.

df.residual

Residual degrees of freedom.

logLik

The log-likelihood of the model. [stats::logLik()] may be a useful reference.

nobs

Number of observations used.

p.value

P-value corresponding to the test statistic.

r.squared

R squared statistic, or the percent of variation explained by the model. Also known as the coefficient of determination.

sigma

Estimated standard error of the residuals.

statistic

Test statistic.

## Examples


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 x 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-12tidy(coeftest(m, conf.int = TRUE))
#> # A tibble: 2 x 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)
tidy(coeftest(m, vcov = vcovHC))               # "HC3" (default) robust SEs
#> # A tibble: 2 x 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-12tidy(coeftest(m, vcov = vcovHC, type = "HC2")) # "HC2" robust SEs
#> # A tibble: 2 x 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-12tidy(coeftest(m, vcov = NeweyWest))            # N-W HAC robust SEs
#> # A tibble: 2 x 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))
#> Original model not retained as part of coeftest object. For additional model summary information (r.squared, df, etc.), consider passing glance.coeftest() an object where the underlying model has been saved, i.e.lmtest::coeftest(..., save = TRUE).
#> This message is displayed once per session.#> # A tibble: 1 x 4
#>   logLik     AIC   BIC  nobs
#>   <chr>    <dbl> <dbl> <int>
#> 1 -206.578  419.  425.    50glance(coeftest(m, save = TRUE)) # More columns
#> # A tibble: 1 x 12
#>   r.squared adj.r.squared sigma statistic  p.value    df logLik   AIC   BIC
#>       <dbl>         <dbl> <dbl>     <dbl>    <dbl> <dbl>  <dbl> <dbl> <dbl>
#> 1     0.651         0.644  15.4      89.6 1.49e-12     1  -207.  419.  425.
#> # … with 3 more variables: deviance <dbl>, df.residual <int>, nobs <int>