ModelBinaryChoice: A light weight implementation of a logistic regression model.

In dymium-org/dymiumModel: Extra model classes that are compatible with dymiumCore

Description

A binary logistic model.

Super classes

dymiumCore::Generic -> dymiumCore::Model -> dymiumModel::ModelBase -> ModelBinaryChoice

Methods

Public methods

• ModelBinaryChoice$new()

• ModelBinaryChoice$predict()

• ModelBinaryChoice$clone()

Inherited methods

• dymiumCore::Generic$debug()

• dymiumCore::Generic$is_dymium_class()

• dymiumCore::Generic$log()

• dymiumCore::Model$class()

• dymiumCore::Model$get()

• dymiumCore::Model$modify()

• dymiumCore::Model$set()

• dymiumCore::Model$simulate()

• dymiumModel::ModelBase$print()

• dymiumModel::ModelBase$summary()

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Method new()

Initialisation function

Usage

ModelBinaryChoice$new(params, formula, preprocessing_fn = NULL)

Arguments

params

a data.frame object.

formula

a formula object.

preprocessing_fn

a pre-processing function that gets applied to the data given to the predict method before making the prediction.

Returns

NULL

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Method predict()

This predict method returns probabilities generated from the parameters of this Model object.

Usage

ModelBinaryChoice$predict(newdata, link_function = c("logit"))

Arguments

newdata

a data.frame object.

link_function

:: character(1)
default as 'logit' using stats::binomial(link = "logit"). Choice of 'logit' and 'probit'. TODO: implement 'probit' option.

Returns

a numeric vector.

---

Method clone()

The objects of this class are cloneable with this method.

Usage

ModelBinaryChoice$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

.data <- cars
.data$fast_car <- ifelse(.data$speed > 20, 1, 0)
glm_model <- glm(fast_car ~ dist, data = .data)
Mod <- ModelBinaryChoice$new(glm_model$coefficients, formula = glm_model$formula)
Mod
head(Mod$predict(.data))

dymium-org/dymiumModel documentation built on June 23, 2020, 11:01 a.m.