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model_categorical_glm

Source: R/model_categorical_glm.R
model_categorical_glm.Rd

model_categorical_glm

Usage

model_categorical_glm(x, y)

Arguments

x

features/predictors used in prediction of outcome

y

categorical outcome to predict

Value

Fitted binomial GLM or multinomial model.

See also

Other categorical model wrappers: model_categorical_all(), model_categorical_elastic_net(), model_categorical_lasso(), model_categorical_ridge(), model_categorical_svm()

Examples

x <- iris[, c("Sepal.Length", "Sepal.Width", "Petal.Length", "Petal.Width")]
y <- iris$Species
model_categorical_glm(x, y)
#> # 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 = y ~ x)
#> 
#> Coefficients:
#>            (Intercept) xSepal.Length xSepal.Width xPetal.Length xPetal.Width
#> versicolor    18.69037     -5.458424    -8.707401      14.24477    -3.097684
#> virginica    -23.83628     -7.923634   -15.370769      23.65978    15.135301
#> 
#> Residual Deviance: 11.89973 
#> AIC: 31.89973