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 multinom
glance(x, ...)

Arguments

x

A multinom object returned from nnet::multinom().

...

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

glance(), nnet::multinom()

Other multinom tidiers: tidy.multinom

Value

A tibble::tibble() with exactly one row and columns:

AIC

Akaike's Information Criterion for the model.

deviance

Deviance of the model.

edf

The effective degrees of freedom

Examples

library(nnet) library(MASS) example(birthwt)
#> #> brthwt> bwt <- with(birthwt, { #> brthwt+ race <- factor(race, labels = c("white", "black", "other")) #> brthwt+ ptd <- factor(ptl > 0) #> brthwt+ ftv <- factor(ftv) #> brthwt+ levels(ftv)[-(1:2)] <- "2+" #> brthwt+ data.frame(low = factor(low), age, lwt, race, smoke = (smoke > 0), #> brthwt+ ptd, ht = (ht > 0), ui = (ui > 0), ftv) #> brthwt+ }) #> #> brthwt> options(contrasts = c("contr.treatment", "contr.poly")) #> #> brthwt> glm(low ~ ., binomial, bwt) #> #> Call: glm(formula = low ~ ., family = binomial, data = bwt) #> #> Coefficients: #> (Intercept) age lwt raceblack raceother smokeTRUE #> 0.82302 -0.03723 -0.01565 1.19241 0.74068 0.75553 #> ptdTRUE htTRUE uiTRUE ftv1 ftv2+ #> 1.34376 1.91317 0.68020 -0.43638 0.17901 #> #> Degrees of Freedom: 188 Total (i.e. Null); 178 Residual #> Null Deviance: 234.7 #> Residual Deviance: 195.5 AIC: 217.5
bwt.mu <- multinom(low ~ ., bwt)
#> # weights: 12 (11 variable) #> initial value 131.004817 #> iter 10 value 98.029803 #> final value 97.737759 #> converged
tidy(bwt.mu)
#> # A tibble: 11 x 6 #> y.level term estimate std.error statistic p.value #> <chr> <chr> <dbl> <dbl> <dbl> <dbl> #> 1 1 (Intercept) 2.28 1.24 0.661 0.508 #> 2 1 age 0.963 0.0387 -0.962 0.336 #> 3 1 lwt 0.984 0.00708 -2.21 0.0271 #> 4 1 raceblack 3.29 0.536 2.22 0.0261 #> 5 1 raceother 2.10 0.462 1.60 0.109 #> 6 1 smokeTRUE 2.13 0.425 1.78 0.0755 #> 7 1 ptdTRUE 3.83 0.481 2.80 0.00518 #> 8 1 htTRUE 6.77 0.721 2.65 0.00794 #> 9 1 uiTRUE 1.97 0.464 1.46 0.143 #> 10 1 ftv1 0.646 0.479 -0.910 0.363 #> 11 1 ftv2+ 1.20 0.456 0.392 0.695
glance(bwt.mu)
#> # A tibble: 1 x 3 #> edf deviance AIC #> <dbl> <dbl> <dbl> #> 1 11 195. 217.
#* This model is a truly terrible model #* but it should show you what the output looks #* like in a multinomial logistic regression fit.gear <- multinom(gear ~ mpg + factor(am), data = mtcars)
#> # weights: 12 (6 variable) #> initial value 35.155593 #> iter 10 value 14.156582 #> iter 20 value 14.031881 #> iter 30 value 14.025659 #> iter 40 value 14.021414 #> iter 50 value 14.019824 #> iter 60 value 14.019278 #> iter 70 value 14.018601 #> iter 80 value 14.018282 #> iter 80 value 14.018282 #> iter 90 value 14.017126 #> final value 14.015374 #> converged
tidy(fit.gear)
#> # A tibble: 6 x 6 #> y.level term estimate std.error statistic p.value #> <chr> <chr> <dbl> <dbl> <dbl> <dbl> #> 1 4 (Intercept) 1.44e-5 5.32 -2.10 3.60e- 2 #> 2 4 mpg 1.69e+0 0.268 1.96 5.02e- 2 #> 3 4 factor(am)1 1.47e+5 66.9 0.178 8.59e- 1 #> 4 5 (Intercept) 1.03e-8 67.9 -0.271 7.87e- 1 #> 5 5 mpg 1.44e+0 0.292 1.25 2.10e- 1 #> 6 5 factor(am)1 5.58e+9 2.17 10.3 4.54e-25
glance(fit.gear)
#> # A tibble: 1 x 3 #> edf deviance AIC #> <dbl> <dbl> <dbl> #> 1 6 28.0 40.0