Aliases: glance.multinom
Keywords:
### ** Examples # feel free to ignore the following line—it allows {broom} to supply # examples without requiring the model-supplying package to be installed. if (requireNamespace("nnet", quietly = TRUE)) { if (requireNamespace("MASS", quietly = TRUE)) { # load libraries for models and data library(nnet) library(MASS) example(birthwt) bwt.mu <- multinom(low ~ ., bwt) tidy(bwt.mu) glance(bwt.mu) # or, for output from a multinomial logistic regression fit.gear <- multinom(gear ~ mpg + factor(am), data = mtcars) tidy(fit.gear) glance(fit.gear) } }
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 # weights: 12 (11 variable) initial value 131.004817 iter 10 value 98.029803 final value 97.737759 converged # 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
# A tibble: 1 × 4 edf deviance AIC nobs <dbl> <dbl> <dbl> <int> 1 6 28.0 40.0 32