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 clmm
glance(x, ...)

Arguments

x

A clmm object returned from ordinal::clmm().

...

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.

edf

The effective degrees of freedom

logLik

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

nobs

Number of observations used.

Examples

library(ordinal) fit <- clmm(rating ~ temp + contact + (1|judge), data = wine) tidy(fit)
#> # A tibble: 6 x 6 #> term estimate std.error statistic p.value coef.type #> <chr> <dbl> <dbl> <dbl> <dbl> <chr> #> 1 1|2 -1.62 0.682 -2.38 1.74e- 2 intercept #> 2 2|3 1.51 0.604 2.51 1.22e- 2 intercept #> 3 3|4 4.23 0.809 5.23 1.72e- 7 intercept #> 4 4|5 6.09 0.972 6.26 3.82e-10 intercept #> 5 tempwarm 3.06 0.595 5.14 2.68e- 7 location #> 6 contactyes 1.83 0.513 3.58 3.44e- 4 location
tidy(fit, conf.int = TRUE, conf.level = 0.9)
#> # A tibble: 6 x 8 #> term estimate std.error statistic p.value conf.low conf.high coef.type #> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr> #> 1 1|2 -1.62 0.682 -2.38 1.74e- 2 -2.75 -0.501 intercept #> 2 2|3 1.51 0.604 2.51 1.22e- 2 0.520 2.51 intercept #> 3 3|4 4.23 0.809 5.23 1.72e- 7 2.90 5.56 intercept #> 4 4|5 6.09 0.972 6.26 3.82e-10 4.49 7.69 intercept #> 5 tempwarm 3.06 0.595 5.14 2.68e- 7 2.08 4.04 location #> 6 contactyes 1.83 0.513 3.58 3.44e- 4 0.992 2.68 location
tidy(fit, conf.int = TRUE, exponentiate = TRUE)
#> # A tibble: 6 x 8 #> term estimate std.error statistic p.value conf.low conf.high coef.type #> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr> #> 1 1|2 0.197 0.682 -2.38 1.74e- 2 0.0518 0.751 intercept #> 2 2|3 4.54 0.604 2.51 1.22e- 2 1.39 14.8 intercept #> 3 3|4 68.6 0.809 5.23 1.72e- 7 14.1 335. intercept #> 4 4|5 441. 0.972 6.26 3.82e-10 65.5 2965. intercept #> 5 tempwarm 21.4 0.595 5.14 2.68e- 7 6.66 68.7 location #> 6 contactyes 6.26 0.513 3.58 3.44e- 4 2.29 17.1 location
glance(fit)
#> # A tibble: 1 x 5 #> edf AIC BIC logLik nobs #> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 7 177. 193. -81.6 72
fit2 <- clmm(rating ~ temp + (1|judge), nominal = ~ contact, data = wine)
#> Warning: unrecognized control elements named ‘nominal’ ignored
tidy(fit2)
#> # A tibble: 5 x 6 #> term estimate std.error statistic p.value coef.type #> <chr> <dbl> <dbl> <dbl> <dbl> <chr> #> 1 1|2 -2.20 0.613 -3.59 0.000333 intercept #> 2 2|3 0.545 0.476 1.15 0.252 intercept #> 3 3|4 2.84 0.607 4.68 0.00000291 intercept #> 4 4|5 4.48 0.751 5.96 0.00000000256 intercept #> 5 tempwarm 2.67 0.554 4.81 0.00000147 location
glance(fit2)
#> # A tibble: 1 x 5 #> edf AIC BIC logLik nobs #> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 6 189. 203. -88.7 72