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 modelling function. This includes the name of the modelling function or any arguments passed to the modelling 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.

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

Arguments

x

A lmrob object returned from robustbase::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. 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.

Details

For tidiers for robust models from the MASS package see tidy.rlm().

See also

Value

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

df.residual

Residual degrees of freedom.

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

library(robustbase) # From the robustbase::lmrob examples: data(coleman) set.seed(0) m <- robustbase::lmrob(Y ~ ., data=coleman) tidy(m)
#> # A tibble: 6 x 5 #> term estimate std.error statistic p.value #> <chr> <dbl> <dbl> <dbl> <dbl> #> 1 (Intercept) 30.5 6.71 4.54 4.59e- 4 #> 2 salaryP -1.67 0.431 -3.86 1.72e- 3 #> 3 fatherWc 0.0843 0.0147 5.74 5.10e- 5 #> 4 sstatus 0.668 0.0339 19.7 1.30e-11 #> 5 teacherSc 1.17 0.110 10.6 4.35e- 8 #> 6 motherLev -4.14 0.921 -4.49 5.07e- 4
#> # A tibble: 20 x 8 #> Y salaryP fatherWc sstatus teacherSc motherLev .fitted .resid #> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 37.0 3.83 28.9 7.2 26.6 6.19 36.8 -0.191 #> 2 26.5 2.89 20.1 -11.7 24.4 5.17 26.7 0.159 #> 3 36.5 2.86 69.0 12.3 25.7 7.04 40.7 4.16 #> 4 40.7 2.92 65.4 14.3 25.7 7.1 41.3 0.625 #> 5 37.1 3.06 29.6 6.31 25.4 6.15 36.3 -0.768 #> 6 33.9 2.07 44.8 6.16 21.6 6.41 33.7 -0.249 #> 7 41.8 2.52 77.4 12.7 24.9 6.86 42.0 0.203 #> 8 33.4 2.45 24.7 -0.17 25.0 5.78 33.7 0.282 #> 9 41.0 3.13 65.0 9.85 26.6 6.51 41.5 0.466 #> 10 37.2 2.44 9.99 -0.05 28.0 5.57 36.9 -0.286 #> 11 23.3 2.09 12.2 -12.9 23.5 5.62 23.7 0.368 #> 12 35.2 2.52 22.6 0.92 23.6 5.34 34.3 -0.912 #> 13 34.9 2.22 14.3 4.77 24.5 5.8 35.8 0.924 #> 14 33.1 2.67 31.8 -0.96 25.8 6.19 32.6 -0.486 #> 15 22.7 2.71 11.6 -16.0 25.2 5.62 22.4 -0.266 #> 16 39.7 3.14 68.5 10.6 25.0 6.94 38.6 -1.07 #> 17 31.8 3.54 42.6 2.66 25.0 6.33 33.0 1.19 #> 18 31.7 2.52 16.7 -11.0 24.8 6.01 24.5 -7.23 #> 19 43.1 2.68 86.3 15.0 25.5 7.51 42.1 -1.03 #> 20 41.0 2.37 76.7 12.8 24.5 6.96 41.4 0.367
#> # A tibble: 1 x 3 #> r.squared sigma df.residual #> <dbl> <dbl> <int> #> 1 0.981 1.13 14
# From the robustbase::glmrob examples: data(carrots) Rfit <- glmrob(cbind(success, total-success) ~ logdose + block, family = binomial, data = carrots, method= "Mqle", control= glmrobMqle.control(tcc=1.2)) tidy(Rfit)
#> # A tibble: 4 x 5 #> term estimate std.error statistic p.value #> <chr> <dbl> <dbl> <dbl> <dbl> #> 1 (Intercept) 2.39 0.692 3.45 0.000561 #> 2 logdose -2.05 0.368 -5.56 0.0000000268 #> 3 blockB2 0.235 0.212 1.11 0.268 #> 4 blockB3 -0.450 0.241 -1.87 0.0620
augment(Rfit)
#> # A tibble: 24 x 4 #> `cbind(success, total - success)`[,"success"] [,""] logdose block .fitted #> <int> <int> <dbl> <fct> <dbl> #> 1 10 25 1.52 B1 -0.726 #> 2 16 26 1.64 B1 -0.972 #> 3 8 42 1.76 B1 -1.22 #> 4 6 36 1.88 B1 -1.46 #> 5 9 26 2 B1 -1.71 #> 6 9 33 2.12 B1 -1.96 #> 7 1 31 2.24 B1 -2.20 #> 8 2 26 2.36 B1 -2.45 #> 9 17 21 1.52 B2 -0.491 #> 10 10 30 1.64 B2 -0.737 #> # … with 14 more rows