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, ...)

x | A |
---|---|

... | Additional arguments. Not used. Needed to match generic
signature only. |

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).

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

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.

Akaike's Information Criterion for the model.

Bayesian Information Criterion for the model.

Deviance of the model.

Degrees of freedom used by the model.

Residual degrees of freedom.

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

Number of observations used.

P-value corresponding to the test statistic.

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

Estimated standard error of the residuals.

Test statistic.

#> #> 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 #>#> # 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 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) tidy(coeftest(m, vcov = vcovHC)) # "HC3" (default) robust SEs#> # 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#> # 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#> # 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#> # A tibble: 1 × 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>