LikLogis: Logistic Likelihood

In duckmayr/gpmss: Gaussian Process Models for Social Science

Description

The likelihood of y_i given f_i is given by the logistic function with input y_i f_i (where the binary labels for y are encoded by 1 and -1). It takes no hyperparameters.

Details

The log likelihood is given by

∑_i -log ( 1 + exp( -y_i f_i ) )

Super class

gpmss::LikelihoodFunction -> LikLogis

Public fields

name

A character vector of length one giving the likelihood function's name; "logistic"

hypers

A numeric vector giving the likelihood function's hyperparameters

Methods

Public methods

• LikLogis$lp()

• LikLogis$f_derivative()

• LikLogis$clone()

Inherited methods

• gpmss::LikelihoodFunction$initialize()

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Method lp()

Compute log probability of outcomes given function values

Usage

LikLogis$lp(y, f, hypers = NULL)

Arguments

y

The observed outcomes

f

The function values (generally a posterior mean or mode)

hypers

A numeric vector giving hyperparameters for the likelihood function. If NULL (the default), the hypers data member is used.

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Method f_derivative()

Compute partial derivatives of log likelihood function with respect to f

Usage

LikLogis$f_derivative(y, f, hypers = NULL, order = 1)

Arguments

y

The observed outcomes

f

The function values (generally a posterior mean or mode)

hypers

A numeric vector giving hyperparameters for the likelihood function. If NULL (the default), the hypers data member is used.

order

An integer vector of length one giving order of derivative desired; the default is 1

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Method clone()

The objects of this class are cloneable with this method.

Usage

LikLogis$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

duckmayr/gpmss documentation built on Nov. 8, 2021, 5:48 a.m.