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 'mlogit'
glance(x, ...)Arguments
- x
an object returned from
mlogit::mlogit().- ...
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 mlogit tidiers:
augment.mlogit(),
tidy.mlogit()
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.
- logLik
The log-likelihood of the model. [stats::logLik()] may be a useful reference.
- nobs
Number of observations used.
- rho2
McFadden's rho squared with respect to a market shares (constants-only) model.
- rho20
McFadden's rho squared with respect to an equal shares (no information) model.
Examples
# load libraries for models and data
library(mlogit)
data("Fishing", package = "mlogit")
Fish <- dfidx(Fishing, varying = 2:9, shape = "wide", choice = "mode")
# fit model
m <- mlogit(mode ~ price + catch | income, data = Fish)
# summarize model fit with tidiers
tidy(m)
#> # A tibble: 8 × 5
#> term estimate std.error statistic p.value
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 (Intercept):boat 0.527 0.223 2.37 1.79e- 2
#> 2 (Intercept):charter 1.69 0.224 7.56 3.95e-14
#> 3 (Intercept):pier 0.778 0.220 3.53 4.18e- 4
#> 4 price -0.0251 0.00173 -14.5 0
#> 5 catch 0.358 0.110 3.26 1.12e- 3
#> 6 income:boat 0.0000894 0.0000501 1.79 7.40e- 2
#> 7 income:charter -0.0000333 0.0000503 -0.661 5.08e- 1
#> 8 income:pier -0.000128 0.0000506 -2.52 1.18e- 2
augment(m)
#> # A tibble: 4,728 × 9
#> id alternative chosen price catch income .probability .fitted
#> <int> <fct> <lgl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 beach FALSE 158. 0.0678 7083. 0.125 -3.94
#> 2 1 boat FALSE 158. 0.260 7083. 0.427 -2.71
#> 3 1 charter TRUE 183. 0.539 7083. 0.339 -2.94
#> 4 1 pier FALSE 158. 0.0503 7083. 0.109 -4.07
#> 5 2 beach FALSE 15.1 0.105 1250. 0.116 -0.342
#> 6 2 boat FALSE 10.5 0.157 1250. 0.251 0.431
#> 7 2 charter TRUE 34.5 0.467 1250. 0.423 0.952
#> 8 2 pier FALSE 15.1 0.0451 1250. 0.210 0.255
#> 9 3 beach FALSE 162. 0.533 3750. 0.00689 -3.87
#> 10 3 boat TRUE 24.3 0.241 3750. 0.465 0.338
#> # ℹ 4,718 more rows
#> # ℹ 1 more variable: .resid <dbl>
glance(m)
#> # A tibble: 1 × 6
#> logLik rho2 rho20 AIC BIC nobs
#> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
#> 1 -1215. 0.189 0.258 2446. NA 1182