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 class 'clm'
glance(x, ...)Arguments
- x
A
clmobject returned fromordinal::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 passconf.lvel = 0.9, all computation will proceed usingconf.level = 0.95. Two exceptions here are:
See also
Other ordinal tidiers:
augment.clm(),
augment.polr(),
glance.clmm(),
glance.polr(),
glance.svyolr(),
tidy.clm(),
tidy.clmm(),
tidy.polr(),
tidy.svyolr()
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
# load libraries for models and data
library(ordinal)
# fit model
fit <- clm(rating ~ temp * contact, data = wine)
# summarize model fit with tidiers
tidy(fit)
#> # A tibble: 7 × 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 × 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 temp… 2.32 0.701 3.31 9.28e- 4 1.20 3.52 location
#> 6 cont… 1.35 0.660 2.04 4.13e- 2 0.284 2.47 location
#> 7 temp… 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 × 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 temp… 10.2 0.701 3.31 9.28e- 4 2.58 40.2 location
#> 6 cont… 3.85 0.660 2.04 4.13e- 2 1.05 14.0 location
#> 7 temp… 1.43 0.924 0.389 6.97e- 1 0.234 8.76 location
glance(fit)
#> # A tibble: 1 × 6
#> edf AIC BIC logLik df.residual nobs
#> <int> <dbl> <dbl> <logLik> <dbl> <dbl>
#> 1 7 187. 203. -86.4162 65 72
augment(fit, type.predict = "prob")
#> # A tibble: 72 × 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
#> # ℹ 62 more rows
augment(fit, type.predict = "class")
#> # A tibble: 72 × 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
#> # ℹ 62 more rows
# ...and again with another model specification
fit2 <- clm(rating ~ temp, nominal = ~contact, data = wine)
tidy(fit2)
#> # A tibble: 9 × 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 × 6
#> edf AIC BIC logLik df.residual nobs
#> <int> <dbl> <dbl> <logLik> <dbl> <dbl>
#> 1 9 190. 211. -86.20855 63 72