Parametric regression model (abstract class)

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.

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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

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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

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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

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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

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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

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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

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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

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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.