coef.Model: Model coefficients

In brandon-mosqueda/SKM: Sparse Kernels Methods

Model coefficients

Description

Extracts the model coefficients from a fitted model object.

Usage

## S3 method for class 'Model'
coef(model)

Arguments

model

(Model) An object of a fitted model.

Details

The only models you can extract the coefficients are: generalized_linear_model(), random_forest(), partial_least_squares() and bayesian_model().

Value

For univariate models

When the response variable is a numeric response, a named vector is returned where each element maps to an element of the predictors matrix. Some models' coefficients includes an intercept too.

When the response variable is categorical, including binary, a numeric matrix is returned where the columns maps to the columns of the predictors matrix and the rows corresponds to the different classes. Some models' coefficients includes an additional row with the overall coefficient.

For multivariate models

A named list with an element for each response variable in the fitted model. Each element contains a vector with the coefficients of each response.

Examples

## Not run: 
# Univariate analysis -------------------------------------------------------
x <- to_matrix(iris[, -5])
y <- iris$Species
model <- random_forest(x, y)

# Obtain the variables importance
coef(model)

# Multivariate analysis -----------------------------------------------------
x <- to_matrix(iris[, -c(1, 2)])
y <- iris[, c(1, 2)]
model <- generalized_linear_model(x, y)

# Obtain the models' coefficients
coef(model)

## End(Not run)

brandon-mosqueda/SKM documentation built on Feb. 8, 2025, 5:24 p.m.