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 polr
glance(x, ...)

Arguments

x

A polr object returned from MASS::polr().

...

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.

deviance

Deviance of 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(MASS) fit <- polr(Sat ~ Infl + Type + Cont, weights = Freq, data = housing) tidy(fit, exponentiate = TRUE, conf.int = TRUE)
#> #> Re-fitting to get Hessian
#> Waiting for profiling to be done...
#> #> Re-fitting to get Hessian
#> # A tibble: 8 x 7 #> term estimate std.error statistic conf.low conf.high coef.type #> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <chr> #> 1 InflMedium 1.76 0.105 5.41 1.44 2.16 coefficient #> 2 InflHigh 3.63 0.127 10.1 2.83 4.66 coefficient #> 3 TypeApartment 0.564 0.119 -4.80 0.446 0.712 coefficient #> 4 TypeAtrium 0.693 0.155 -2.36 0.511 0.940 coefficient #> 5 TypeTerrace 0.336 0.151 -7.20 0.249 0.451 coefficient #> 6 ContHigh 1.43 0.0955 3.77 1.19 1.73 coefficient #> 7 Low|Medium 0.609 0.125 -3.97 NA NA scale #> 8 Medium|High 2.00 0.125 5.50 NA NA scale
glance(fit)
#> # A tibble: 1 x 7 #> edf logLik AIC BIC deviance df.residual nobs #> <int> <dbl> <dbl> <dbl> <dbl> <int> <int> #> 1 8 -1740. 3495. 3539. 3479. 1673 1681
augment(fit, type.predict = "class")
#> # A tibble: 72 x 6 #> Sat Infl Type Cont `(weights)` .fitted #> <ord> <fct> <fct> <fct> <int> <fct> #> 1 Low Low Tower Low 21 Low #> 2 Medium Low Tower Low 21 Low #> 3 High Low Tower Low 28 Low #> 4 Low Medium Tower Low 34 High #> 5 Medium Medium Tower Low 22 High #> 6 High Medium Tower Low 36 High #> 7 Low High Tower Low 10 High #> 8 Medium High Tower Low 11 High #> 9 High High Tower Low 36 High #> 10 Low Low Apartment Low 61 Low #> # … with 62 more rows