Multinomial Logistic Regression Sample, R

Multinomial Logistic Regression Sample

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'data.frame': 150 obs. of 5 variables: $ Sepal.Length: num 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ... $ Sepal.Width : num 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ... $ Petal.Length: num 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ... $ Petal.Width : num 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ... $ Species : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ... [1] "Sepal.Length" "Sepal.Width" "Petal.Length" "Petal.Width" "Species" [1] "setosa" "versicolor" "virginica" # weights: 18 (10 variable) initial value 164.791843 iter 10 value 16.177348 iter 20 value 7.111438 iter 30 value 6.182999 iter 40 value 5.984028 iter 50 value 5.961278 iter 60 value 5.954900 iter 70 value 5.951851 iter 80 value 5.950343 iter 90 value 5.949904 iter 100 value 5.949867 final value 5.949867 stopped after 100 iterations Call: multinom(formula = DV ~ Sepal.Length + Sepal.Width + Petal.Length + Petal.Width) Coefficients: (Intercept) Sepal.Length Sepal.Width Petal.Length Petal.Width versicolor 18.69037 -5.458424 -8.707401 14.24477 -3.097684 virginica -23.83628 -7.923634 -15.370769 23.65978 15.135301 Std. Errors: (Intercept) Sepal.Length Sepal.Width Petal.Length Petal.Width versicolor 34.97116 89.89215 157.0415 60.19170 45.48852 virginica 35.76649 89.91153 157.1196 60.46753 45.93406 Residual Deviance: 11.89973 AIC: 31.89973 setosa versicolor virginica setosa 50 0 0 versicolor 0 49 1 virginica 0 1 49