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 betareg
glance(x, ...)Arguments
- x
A
betaregobject produced by a call tobetareg::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 passconf.lvel = 0.9, all computation will proceed usingconf.level = 0.95. Two exceptions here are:
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
Degrees of freedom used by the null model.
- df.residual
Residual degrees of freedom.
- logLik
The log-likelihood of the model. [stats::logLik()] may be a useful reference.
- nobs
Number of observations used.
- pseudo.r.squared
Like the R squared statistic, but for situations when the R squared statistic isn't defined.
Examples
# load libraries for models and data
library(betareg)
# load dats
data("GasolineYield", package = "betareg")
# fit model
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
#> -6.15957 1.72773 1.32260 1.57231 1.05971
#> batch5 batch6 batch7 batch8 batch9
#> 1.13375 1.04016 0.54369 0.49590 0.38579
#> temp
#> 0.01097
#>
#> Phi coefficients (precision model with identity link):
#> (phi)
#> 440.3
#>
# summarize model fit with tidiers
tidy(mod)
#> # A tibble: 12 × 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 × 8
#> component term estimate std.error statistic p.value conf.low
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 mean (Intercept) -6.16 0.182 -33.8 3.44e-250 -6.52
#> 2 mean batch1 1.73 0.101 17.1 2.59e- 65 1.53
#> 3 mean batch2 1.32 0.118 11.2 3.34e- 29 1.09
#> 4 mean batch3 1.57 0.116 13.5 8.81e- 42 1.34
#> 5 mean batch4 1.06 0.102 10.4 4.06e- 25 0.859
#> 6 mean batch5 1.13 0.104 11.0 6.52e- 28 0.931
#> 7 mean batch6 1.04 0.106 9.81 1.03e- 22 0.832
#> 8 mean batch7 0.544 0.109 4.98 6.29e- 7 0.330
#> 9 mean batch8 0.496 0.109 4.55 5.30e- 6 0.282
#> 10 mean batch9 0.386 0.119 3.25 1.14e- 3 0.153
#> 11 mean temp 0.0110 0.000413 26.6 1.26e-155 0.0102
#> 12 precision (phi) 440. 110. 4.00 6.29e- 5 225.
#> # ℹ 1 more variable: conf.high <dbl>
tidy(mod, conf.int = TRUE, conf.level = .99)
#> # A tibble: 12 × 8
#> component term estimate std.error statistic p.value conf.low
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 mean (Intercept) -6.16 0.182 -33.8 3.44e-250 -6.63
#> 2 mean batch1 1.73 0.101 17.1 2.59e- 65 1.47
#> 3 mean batch2 1.32 0.118 11.2 3.34e- 29 1.02
#> 4 mean batch3 1.57 0.116 13.5 8.81e- 42 1.27
#> 5 mean batch4 1.06 0.102 10.4 4.06e- 25 0.796
#> 6 mean batch5 1.13 0.104 11.0 6.52e- 28 0.867
#> 7 mean batch6 1.04 0.106 9.81 1.03e- 22 0.767
#> 8 mean batch7 0.544 0.109 4.98 6.29e- 7 0.263
#> 9 mean batch8 0.496 0.109 4.55 5.30e- 6 0.215
#> 10 mean batch9 0.386 0.119 3.25 1.14e- 3 0.0803
#> 11 mean temp 0.0110 0.000413 26.6 1.26e-155 0.00990
#> 12 precision (phi) 440. 110. 4.00 6.29e- 5 157.
#> # ℹ 1 more variable: conf.high <dbl>
augment(mod)
#> # A tibble: 32 × 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
#> # ℹ 22 more rows
glance(mod)
#> # A tibble: 1 × 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