Glance at a(n) lmRob object

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 lmRob
glance(x, ...)

Arguments

x

A lmRob object returned from robust::lmRob().

...

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.

See also

robust::lmRob()

Other robust tidiers: augment.lmRob(), glance.glmRob(), tidy.glmRob(), tidy.lmRob()

Value

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

deviance

Deviance of the model.

df.residual

Residual degrees of freedom.

nobs

Number of observations used.

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.

Examples

# load modeling library
library(robust)

# fit model
m <- lmRob(mpg ~ wt, data = mtcars)

# summarize model fit with tidiers
tidy(m)
#> # A tibble: 2 × 5
#>   term        estimate std.error statistic  p.value
#>   <chr>          <dbl>     <dbl>     <dbl>    <dbl>
#> 1 (Intercept)    35.6       3.58      9.93 5.37e-11
#> 2 wt             -4.91      1.09     -4.49 9.67e- 5
augment(m)
#> # A tibble: 32 × 4
#>    .rownames           mpg    wt .fitted
#>    <chr>             <dbl> <dbl>   <dbl>
#>  1 Mazda RX4          21    2.62    22.7
#>  2 Mazda RX4 Wag      21    2.88    21.4
#>  3 Datsun 710         22.8  2.32    24.2
#>  4 Hornet 4 Drive     21.4  3.22    19.8
#>  5 Hornet Sportabout  18.7  3.44    18.7
#>  6 Valiant            18.1  3.46    18.6
#>  7 Duster 360         14.3  3.57    18.0
#>  8 Merc 240D          24.4  3.19    19.9
#>  9 Merc 230           22.8  3.15    20.1
#> 10 Merc 280           19.2  3.44    18.7
#> # ℹ 22 more rows
glance(m)
#> # A tibble: 1 × 5
#>   r.squared deviance sigma df.residual  nobs
#>       <dbl>    <dbl> <dbl>       <int> <int>
#> 1     0.567     136.  2.95          30    32