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 betareg
glance(x, ...)

Arguments

x

A betareg object produced by a call to betareg::betareg().

...

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.

See also

Value

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

AIC

Akaike's Information Criterion for the model.

BIC

Bayesian Information Criterion for the model.

df.null

Needs custom info.

df.residual

Residual degrees of freedom for the model.

logLik

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

pseudo.r.squared

TODO

Examples

library(betareg) data("GasolineYield", package = "betareg") mod <- betareg(yield ~ batch + temp, data = GasolineYield) mod
#> #> Call: #> betareg(formula = yield ~ batch + temp, data = GasolineYield) #> #> Coefficients (mean model with logit link): #> (Intercept) batch1 batch2 batch3 batch4 batch5 #> -6.15957 1.72773 1.32260 1.57231 1.05971 1.13375 #> batch6 batch7 batch8 batch9 temp #> 1.04016 0.54369 0.49590 0.38579 0.01097 #> #> Phi coefficients (precision model with identity link): #> (phi) #> 440.3 #>
tidy(mod)
#> # A tibble: 12 x 6 #> component term estimate std.error statistic p.value #> <chr> <chr> <dbl> <dbl> <dbl> <dbl> #> 1 mean (Intercept) -6.16 0.182 -33.8 3.44e-250 #> 2 mean batch1 1.73 0.101 17.1 2.59e- 65 #> 3 mean batch2 1.32 0.118 11.2 3.34e- 29 #> 4 mean batch3 1.57 0.116 13.5 8.81e- 42 #> 5 mean batch4 1.06 0.102 10.4 4.06e- 25 #> 6 mean batch5 1.13 0.104 11.0 6.52e- 28 #> 7 mean batch6 1.04 0.106 9.81 1.03e- 22 #> 8 mean batch7 0.544 0.109 4.98 6.29e- 7 #> 9 mean batch8 0.496 0.109 4.55 5.30e- 6 #> 10 mean batch9 0.386 0.119 3.25 1.14e- 3 #> 11 mean temp 0.0110 0.000413 26.6 1.26e-155 #> 12 precision (phi) 440. 110. 4.00 6.29e- 5
tidy(mod, conf.int = TRUE)
#> # A tibble: 12 x 8 #> component term estimate std.error statistic p.value conf.low conf.high #> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 mean (Inter… -6.16 1.82e-1 -33.8 3.44e-250 -6.52e+0 -5.80 #> 2 mean batch1 1.73 1.01e-1 17.1 2.59e- 65 1.53e+0 1.93 #> 3 mean batch2 1.32 1.18e-1 11.2 3.34e- 29 1.09e+0 1.55 #> 4 mean batch3 1.57 1.16e-1 13.5 8.81e- 42 1.34e+0 1.80 #> 5 mean batch4 1.06 1.02e-1 10.4 4.06e- 25 8.59e-1 1.26 #> 6 mean batch5 1.13 1.04e-1 11.0 6.52e- 28 9.31e-1 1.34 #> 7 mean batch6 1.04 1.06e-1 9.81 1.03e- 22 8.32e-1 1.25 #> 8 mean batch7 0.544 1.09e-1 4.98 6.29e- 7 3.30e-1 0.758 #> 9 mean batch8 0.496 1.09e-1 4.55 5.30e- 6 2.82e-1 0.709 #> 10 mean batch9 0.386 1.19e-1 3.25 1.14e- 3 1.53e-1 0.618 #> 11 mean temp 0.0110 4.13e-4 26.6 1.26e-155 1.02e-2 0.0118 #> 12 precision (phi) 440. 1.10e+2 4.00 6.29e- 5 2.25e+2 656.
tidy(mod, conf.int = TRUE, conf.level = .99)
#> # A tibble: 12 x 8 #> component term estimate std.error statistic p.value conf.low conf.high #> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 mean (Inter… -6.16 1.82e-1 -33.8 3.44e-250 -6.63e+0 -5.69 #> 2 mean batch1 1.73 1.01e-1 17.1 2.59e- 65 1.47e+0 1.99 #> 3 mean batch2 1.32 1.18e-1 11.2 3.34e- 29 1.02e+0 1.63 #> 4 mean batch3 1.57 1.16e-1 13.5 8.81e- 42 1.27e+0 1.87 #> 5 mean batch4 1.06 1.02e-1 10.4 4.06e- 25 7.96e-1 1.32 #> 6 mean batch5 1.13 1.04e-1 11.0 6.52e- 28 8.67e-1 1.40 #> 7 mean batch6 1.04 1.06e-1 9.81 1.03e- 22 7.67e-1 1.31 #> 8 mean batch7 0.544 1.09e-1 4.98 6.29e- 7 2.63e-1 0.825 #> 9 mean batch8 0.496 1.09e-1 4.55 5.30e- 6 2.15e-1 0.776 #> 10 mean batch9 0.386 1.19e-1 3.25 1.14e- 3 8.03e-2 0.691 #> 11 mean temp 0.0110 4.13e-4 26.6 1.26e-155 9.90e-3 0.0120 #> 12 precision (phi) 440. 1.10e+2 4.00 6.29e- 5 1.57e+2 724.
augment(mod)
#> # A tibble: 32 x 6 #> yield batch temp .fitted .resid .cooksd #> * <dbl> <fct> <dbl> <dbl> <dbl> <dbl> #> 1 0.122 1 205 0.101 1.59 0.0791 #> 2 0.223 1 275 0.195 1.66 0.0917 #> 3 0.347 1 345 0.343 0.211 0.00155 #> 4 0.457 1 407 0.508 -2.88 0.606 #> 5 0.08 2 218 0.0797 0.109 0.0000168 #> 6 0.131 2 273 0.137 -0.365 0.00731 #> 7 0.266 2 347 0.263 0.260 0.00523 #> 8 0.074 3 212 0.0943 -1.77 0.0805 #> 9 0.182 3 272 0.167 1.02 0.0441 #> 10 0.304 3 340 0.298 0.446 0.0170 #> # ... with 22 more rows
glance(mod)
#> # A tibble: 1 x 6 #> pseudo.r.squared df.null logLik AIC BIC df.residual #> <dbl> <dbl> <dbl> <dbl> <dbl> <int> #> 1 0.962 30 84.8 -146. -128. 20