Prototyping Multinomial Logit with R

[This article was first published on Yet Another Blog in Statistical Computing » S+/R, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

Recently, I am working on a new modeling proposal based on the competing risk and need to prototype multinomial logit models with R. There are R packages implementing multinomial logit models that I’ve tested, namely nnet and vgam. Model outputs with iris data are shown below.

data(iris)

### method 1: nnet package ###
library(nnet)
mdl1 <- multinom(Species ~ Sepal.Length, data = iris, model = TRUE)
summary(mdl1)

# Coefficients:
#            (Intercept) Sepal.Length
# versicolor   -26.08339     4.816072
# virginica    -38.76786     6.847957
#
# Std. Errors:
#            (Intercept) Sepal.Length
# versicolor    4.889635    0.9069211
# virginica     5.691596    1.0223867

### method 2: vgam package ### 
library(VGAM)
mdl2 <- vglm(Species ~ Sepal.Length, data = iris, multinomial(refLevel = 1)) 
summary(mdl2)

# Coefficients:
#                Estimate Std. Error z value
# (Intercept):1  -26.0819    4.88924 -5.3346
# (Intercept):2  -38.7590    5.69064 -6.8110
# Sepal.Length:1   4.8157    0.90683  5.3105
# Sepal.Length:2   6.8464    1.02222  6.6976

However, in my view, above methods are not flexible for real-world problems. For instance, there is no off-shelf solution for the variable selection for above multinomial logit models. Instead of building one multinomial logit model, we might develop two separate binomial logit models to accomplish the same task.

### method 3: two binary logit models ### 
iris$y <- ifelse(iris$Species == 'setosa', 0, 1)
mdl31 <- glm(y ~ Sepal.Length, data = iris, subset = (Species != 'virginica'), family = binomial)
summary(mdl31)

#  Coefficients:
#              Estimate Std. Error z value Pr(>|z|)    
# (Intercept)   -27.831      5.434  -5.122 3.02e-07 ***
# Sepal.Length    5.140      1.007   5.107 3.28e-07 ***

mdl32 <- glm(y ~ Sepal.Length, data = iris, subset = (Species != 'versicolor'), family = binomial)
summary(mdl32)

# Coefficients:
#              Estimate Std. Error z value Pr(>|z|)    
# (Intercept)   -38.547      9.557  -4.033 5.50e-05 ***
# Sepal.Length    6.805      1.694   4.016 5.91e-05 ***

As shown above, we can get a set of similar estimated parameters by the third approach with much simpler models.


To leave a comment for the author, please follow the link and comment on their blog: Yet Another Blog in Statistical Computing » S+/R.

R-bloggers.com offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)