Aliases: tidy.multinom multinom_tidiers nnet_tidiers
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