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 polr glance(x, ...)
x | A |
---|---|
... | Additional arguments. Not used. Needed to match generic
signature only. Cautionary note: Misspelled arguments will be
absorbed in |
Other ordinal tidiers:
augment.clm()
,
augment.polr()
,
glance.clmm()
,
glance.clm()
,
glance.svyolr()
,
tidy.clmm()
,
tidy.clm()
,
tidy.polr()
,
tidy.svyolr()
A tibble::tibble()
with exactly one row and columns:
Akaike's Information Criterion for the model.
Bayesian Information Criterion for the model.
Deviance of the model.
Residual degrees of freedom.
The effective degrees of freedom.
The log-likelihood of the model. [stats::logLik()] may be a useful reference.
Number of observations used.
library(MASS) fit <- polr(Sat ~ Infl + Type + Cont, weights = Freq, data = housing) tidy(fit, exponentiate = TRUE, conf.int = TRUE)#> #>#> # 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#> # 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#> # 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#> #>#>#>#> # A tibble: 8 x 6 #> term estimate std.error statistic p.value coef.type #> <chr> <dbl> <dbl> <dbl> <lgl> <chr> #> 1 InflMedium 0.566 0.105 5.41 NA coefficient #> 2 InflHigh 1.29 0.127 10.1 NA coefficient #> 3 TypeApartment -0.572 0.119 -4.80 NA coefficient #> 4 TypeAtrium -0.366 0.155 -2.36 NA coefficient #> 5 TypeTerrace -1.09 0.151 -7.20 NA coefficient #> 6 ContHigh 0.360 0.0955 3.77 NA coefficient #> 7 Low|Medium -0.496 0.125 -3.97 NA scale #> 8 Medium|High 0.691 0.125 5.50 NA scale