ParamRegrModel: Parametric regression model (abstract class)

In gofreg: Bootstrap-Based Goodness-of-Fit Tests for Parametric Regression

Parametric regression model (abstract class)

Description

This is the abstract base class for parametric regression model objects like NormalGLM.

Parametric regression models are built around the following key tasks:

• A method fit() to fit the model to given data, i.e. compute the MLE for the model parameters

• Methods f_yx(), F_yx() and mean_yx() to evaluate the conditional density, distribution and regression function

• A method sample_yx() to generate a random sample of response variables following the model given a vector of covariates

Methods

Public methods

• ParamRegrModel$set_params()

• ParamRegrModel$get_params()

• ParamRegrModel$fit()

• ParamRegrModel$f_yx()

• ParamRegrModel$F_yx()

• ParamRegrModel$F1_yx()

• ParamRegrModel$sample_yx()

• ParamRegrModel$mean_yx()

• ParamRegrModel$clone()

---

Method set_params()

Set the value of the model parameters used as default for the class functions.

Usage

ParamRegrModel$set_params(params)

Arguments

params

model parameters to use as default

Returns

The modified object (self), allowing for method chaining.

---

Method get_params()

Returns the value of the model parameters used as default for the class functions.

Usage

ParamRegrModel$get_params()

Returns

model parameters used as default

---

Method fit()

Calculates the maximum likelihood estimator for the model parameters based on given data.

Usage

ParamRegrModel$fit(data, params_init = private$params, loglik = loglik_xy)

Arguments

data

list containing the data to fit the model to

params_init

initial value of the model parameters to use for the optimization (defaults to the fitted parameter values)

loglik

⁠function(data, model, params)⁠ defaults to loglik_xy()

Returns

MLE of the model parameters for the given data, same shape as params_init

---

Method f_yx()

Evaluates the conditional density function.

Usage

ParamRegrModel$f_yx(t, x, params = private$params)

Arguments

t

value(s) at which the conditional density shall be evaluated

x

vector of covariates

params

model parameters to use, defaults to the fitted parameter values

Returns

value(s) of the conditional density function, same shape as t

---

Method F_yx()

Evaluates the conditional distribution function.

Usage

ParamRegrModel$F_yx(t, x, params = private$params)

Arguments

t

value(s) at which the conditional distribution shall be evaluated

x

vector of covariates

params

model parameters to use, defaults to the fitted parameter values

Returns

value(s) of the conditional distribution function, same shape as t

---

Method F1_yx()

Evaluates the conditional quantile function.

Usage

ParamRegrModel$F1_yx(t, x, params = private$params)

Arguments

t

value(s) at which the conditional quantile function shall be evaluated

x

vector of covariates

params

model parameters to use, defaults to the fitted parameter values

Returns

value(s) of the conditional quantile function, same shape as t

---

Method sample_yx()

Generates a new sample of response variables with the same conditional distribution.

Usage

ParamRegrModel$sample_yx(x, params = private$params)

Arguments

x

vector of covariates

params

model parameters to use, defaults to the fitted parameter values

Returns

vector of sampled response variables, same length as x

---

Method mean_yx()

Evaluates the regression function or in other terms the expected value of Y given X=x.

Usage

ParamRegrModel$mean_yx(x, params = private$params)

Arguments

x

vector of covariates

params

model parameters to use, defaults to the fitted parameter values

Returns

value of the regression function

---

Method clone()

The objects of this class are cloneable with this method.

Usage

ParamRegrModel$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

gofreg documentation built on Oct. 4, 2024, 5:10 p.m.