VGAM Package in R for Multinomial Logistic Regression
The VGAM (Vector Generalized Linear and Additive Models) package in R Programming Language provides a suite of functions for fitting a variety of regression models. The vglm() function is one of the most commonly used functions in the package and can be used for multinomial logistic regression.
R
library(VGAM) # Load the iris dataset data(iris) # Convert the species variable to a factor iris$Species <- as.factor(iris$Species) # Fit a multinomial logistic regression model fit <- vglm(Species ~ Sepal.Length + Sepal.Width + Petal.Length + Petal.Width, data = iris, family = multinomial) # Print the model summary summary(fit) |
Output:
Estimate Std. Error z value Pr(>|z|) (Intercept):1 35.490 22666.953 NA NA (Intercept):2 42.638 25.708 1.659 0.0972 . Sepal.Length:1 9.495 6729.217 NA NA Sepal.Length:2 2.465 2.394 1.030 0.3032 Sepal.Width:1 12.300 3143.611 NA NA Sepal.Width:2 6.681 4.480 1.491 0.1359 Petal.Length:1 -22.975 4799.227 -0.005 0.9962 Petal.Length:2 -9.429 4.737 NA NA Petal.Width:1 -33.843 7583.502 NA NA Petal.Width:2 -18.286 9.743 NA NA
Multinomial Logistic Regression in R
In this article, we will learn about Multinomial Logistic Regression which can be used when we have more than two categories in the target column. Let’s first start with a little bit brief explanation about the multinomial logistic regression and after this we will move on to the code implementation part by using different packages which are available in R.