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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.

Usage

# S3 method for anova
glance(x, ...)

Arguments

x

An anova object, such as those created by stats::anova(), car::Anova(), car::leveneTest(), or car::linearHypothesis().

...

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.

Note

Note that the output of glance.anova() will vary depending on the initializing anova call. In some cases, it will just return an empty data frame. In other cases, glance.anova() may return columns that are also common to tidy.anova(). This is partly to preserve backwards compatibility with early versions of broom, but also because the underlying anova model yields components that could reasonably be interpreted as goodness-of-fit summaries too.

See also

Value

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

deviance

Deviance of the model.

df.residual

Residual degrees of freedom.

Examples


# fit models
a <- lm(mpg ~ wt + qsec + disp, mtcars)
b <- lm(mpg ~ wt + qsec, mtcars)

mod <- anova(a, b)

# summarize model fit with tidiers
tidy(mod)
#> # A tibble: 2 × 7
#>   term                  df.residual   rss    df    sumsq statistic p.value
#>   <chr>                       <dbl> <dbl> <dbl>    <dbl>     <dbl>   <dbl>
#> 1 mpg ~ wt + qsec + di…          28  195.    NA NA       NA         NA    
#> 2 mpg ~ wt + qsec                29  195.    -1 -0.00102  0.000147   0.990
glance(mod)
#> # A tibble: 1 × 2
#>   deviance df.residual
#>      <dbl>       <dbl>
#> 1     195.          29

# car::linearHypothesis() example
library(car)
mod_lht <- linearHypothesis(a, "wt - disp")
tidy(mod_lht)
#> # A tibble: 1 × 10
#>   term   null.value estimate std.error statistic p.value df.residual   rss
#>   <chr>       <dbl>    <dbl>     <dbl>     <dbl>   <dbl>       <dbl> <dbl>
#> 1 wt - …          0    -5.03      1.23      16.6 3.39e-4          28  195.
#> # … with 2 more variables: df <dbl>, sumsq <dbl>
glance(mod_lht)
#> # A tibble: 1 × 2
#>   deviance df.residual
#>      <dbl>       <dbl>
#> 1     195.          28