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 betareg glance(x, ...)
x | A |
---|---|
... | Additional arguments. Not used. Needed to match generic
signature only. Cautionary note: Misspelled arguments will be
absorbed in |
A tibble::tibble()
with exactly one row and columns:
Akaike's Information Criterion for the model.
Bayesian Information Criterion for the model.
Degrees of freedom used by the null model.
Residual degrees of freedom.
The log-likelihood of the model. [stats::logLik()] may be a useful reference.
Number of observations used.
Like the R squared statistic, but for situations when the R squared statistic isn't defined.
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 #>#> # 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#> # 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 (Interce… -6.16 1.82e-1 -33.8 3.44e-250 -6.52 -5.80 #> 2 mean batch1 1.73 1.01e-1 17.1 2.59e- 65 1.53 1.93 #> 3 mean batch2 1.32 1.18e-1 11.2 3.34e- 29 1.09 1.55 #> 4 mean batch3 1.57 1.16e-1 13.5 8.81e- 42 1.34 1.80 #> 5 mean batch4 1.06 1.02e-1 10.4 4.06e- 25 0.859 1.26 #> 6 mean batch5 1.13 1.04e-1 11.0 6.52e- 28 0.931 1.34 #> 7 mean batch6 1.04 1.06e-1 9.81 1.03e- 22 0.832 1.25 #> 8 mean batch7 0.544 1.09e-1 4.98 6.29e- 7 0.330 0.758 #> 9 mean batch8 0.496 1.09e-1 4.55 5.30e- 6 0.282 0.709 #> 10 mean batch9 0.386 1.19e-1 3.25 1.14e- 3 0.153 0.618 #> 11 mean temp 0.0110 4.13e-4 26.6 1.26e-155 0.0102 0.0118 #> 12 precision (phi) 440. 1.10e+2 4.00 6.29e- 5 225. 656.#> # 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 (Interc… -6.16 0.182 -33.8 3.44e-250 -6.63e+0 -5.69 #> 2 mean batch1 1.73 0.101 17.1 2.59e- 65 1.47e+0 1.99 #> 3 mean batch2 1.32 0.118 11.2 3.34e- 29 1.02e+0 1.63 #> 4 mean batch3 1.57 0.116 13.5 8.81e- 42 1.27e+0 1.87 #> 5 mean batch4 1.06 0.102 10.4 4.06e- 25 7.96e-1 1.32 #> 6 mean batch5 1.13 0.104 11.0 6.52e- 28 8.67e-1 1.40 #> 7 mean batch6 1.04 0.106 9.81 1.03e- 22 7.67e-1 1.31 #> 8 mean batch7 0.544 0.109 4.98 6.29e- 7 2.63e-1 0.825 #> 9 mean batch8 0.496 0.109 4.55 5.30e- 6 2.15e-1 0.776 #> 10 mean batch9 0.386 0.119 3.25 1.14e- 3 8.03e-2 0.691 #> 11 mean temp 0.0110 0.000413 26.6 1.26e-155 9.90e-3 0.0120 #> 12 precision (phi) 440. 110. 4.00 6.29e- 5 1.57e+2 724.#> # 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#> # A tibble: 1 x 7 #> pseudo.r.squared df.null logLik AIC BIC df.residual nobs #> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <int> #> 1 0.962 30 84.8 -146. -128. 20 32