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 clm
glance(x, ...)

Arguments

x

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

...

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.residual

Residual degrees of freedom.

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 <- clm(rating ~ temp * contact, data = wine) tidy(fit)
#> # A tibble: 7 x 6 #> term estimate std.error statistic p.value coef.type #> <chr> <dbl> <dbl> <dbl> <dbl> <chr> #> 1 1|2 -1.41 0.545 -2.59 9.66e- 3 intercept #> 2 2|3 1.14 0.510 2.24 2.48e- 2 intercept #> 3 3|4 3.38 0.638 5.29 1.21e- 7 intercept #> 4 4|5 4.94 0.751 6.58 4.66e-11 intercept #> 5 tempwarm 2.32 0.701 3.31 9.28e- 4 location #> 6 contactyes 1.35 0.660 2.04 4.13e- 2 location #> 7 tempwarm:contactyes 0.360 0.924 0.389 6.97e- 1 location
tidy(fit, conf.int = TRUE, conf.level = 0.9)
#> # A tibble: 7 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 -1.41 0.545 -2.59 9.66e- 3 NA NA intercept #> 2 2|3 1.14 0.510 2.24 2.48e- 2 NA NA intercept #> 3 3|4 3.38 0.638 5.29 1.21e- 7 NA NA intercept #> 4 4|5 4.94 0.751 6.58 4.66e-11 NA NA intercept #> 5 tempwarm 2.32 0.701 3.31 9.28e- 4 1.20 3.52 location #> 6 contactyes 1.35 0.660 2.04 4.13e- 2 0.284 2.47 location #> 7 tempwarm:c… 0.360 0.924 0.389 6.97e- 1 -1.17 1.89 location
tidy(fit, conf.int = TRUE, conf.type = "Wald", exponentiate = TRUE)
#> # A tibble: 7 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 0.244 0.545 -2.59 9.66e- 3 0.0837 0.710 intercept #> 2 2|3 3.14 0.510 2.24 2.48e- 2 1.16 8.52 intercept #> 3 3|4 29.3 0.638 5.29 1.21e- 7 8.38 102. intercept #> 4 4|5 140. 0.751 6.58 4.66e-11 32.1 610. intercept #> 5 tempwarm 10.2 0.701 3.31 9.28e- 4 2.58 40.2 location #> 6 contactyes 3.85 0.660 2.04 4.13e- 2 1.05 14.0 location #> 7 tempwarm:c… 1.43 0.924 0.389 6.97e- 1 0.234 8.76 location
glance(fit)
#> # A tibble: 1 x 6 #> edf AIC BIC logLik df.residual nobs #> <int> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 7 187. 203. -86.4 65 72
augment(fit, type.predict = "prob")
#> # A tibble: 72 x 4 #> rating temp contact .fitted #> <ord> <fct> <fct> <dbl> #> 1 2 cold no 0.562 #> 2 3 cold no 0.209 #> 3 3 cold yes 0.435 #> 4 4 cold yes 0.0894 #> 5 4 warm no 0.190 #> 6 4 warm no 0.190 #> 7 5 warm yes 0.286 #> 8 5 warm yes 0.286 #> 9 1 cold no 0.196 #> 10 2 cold no 0.562 #> # … with 62 more rows
augment(fit, type.predict = "class")
#> # A tibble: 72 x 4 #> rating temp contact .fitted #> <ord> <fct> <fct> <fct> #> 1 2 cold no 2 #> 2 3 cold no 2 #> 3 3 cold yes 3 #> 4 4 cold yes 3 #> 5 4 warm no 3 #> 6 4 warm no 3 #> 7 5 warm yes 4 #> 8 5 warm yes 4 #> 9 1 cold no 2 #> 10 2 cold no 2 #> # … with 62 more rows
fit2 <- clm(rating ~ temp, nominal = ~ contact, data = wine) tidy(fit2)
#> # A tibble: 9 x 6 #> term estimate std.error statistic p.value coef.type #> <chr> <dbl> <dbl> <dbl> <dbl> <chr> #> 1 1|2.(Intercept) -1.32 0.562 -2.35 0.0186 intercept #> 2 2|3.(Intercept) 1.25 0.475 2.63 0.00866 intercept #> 3 3|4.(Intercept) 3.55 0.656 5.41 0.0000000625 intercept #> 4 4|5.(Intercept) 4.66 0.860 5.42 0.0000000608 intercept #> 5 1|2.contactyes -1.62 1.16 -1.39 0.164 intercept #> 6 2|3.contactyes -1.51 0.591 -2.56 0.0105 intercept #> 7 3|4.contactyes -1.67 0.649 -2.58 0.00985 intercept #> 8 4|5.contactyes -1.05 0.897 -1.17 0.241 intercept #> 9 tempwarm 2.52 0.535 4.71 0.00000250 location
glance(fit2)
#> # A tibble: 1 x 6 #> edf AIC BIC logLik df.residual nobs #> <int> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 9 190. 211. -86.2 63 72