02-03-Regression-Multinomial.Rmd

## Multinomial Logistic Regression {-}


```{r a4, comment=NA, message=FALSE, warning=FALSE, fig.width=10, fig.height=7}

library(nnet)
library(devtools)
library(lattice)

## Using the iris dataset
summary(iris)

## Build a multinomial logistic model from the nnet package
mdl = multinom(Species ~ ., data = iris)

summary(mdl)

## Prediction based on Sepal and Petal values
sample = data.frame(Sepal.Length = 5.5,
                    Sepal.Width = 3,
                    Petal.Length = 2.5,
                    Petal.Width = 2)

round(predict(mdl, sample, type = "probs"))

## Scatterplot Matrix
splom(iris[, 1:4], col = 1:3,
      panel = function(x, y, i, j, groups, ...) {
        panel.points(x, y, col = iris$Species)
        panel.points(sample[1, j], sample[1, i], col = 'blue', pch = 16)
      }, auto.key = TRUE)

```