newRangePriorData: Redefining the Domain

In MoTBFs: Learning Hybrid Bayesian Networks using Mixtures of Truncated Basis Functions

Redefining the Domain

Description

Computes the new domain of two datasets.

Usage

newRangePriorData(fPI, priorData, N, domain, s, POTENTIAL_TYPE)

Arguments

fPI	The function fitted to the prior data, of class "motbf".
priorData	A "numeric" array with the values to be included as prior information.
N	A "numeric" value equal to the data size.
domain	A "numeric" array with the domain of the data density.
s	A "numeric" value which is the expert's confidence on the prior information. It is a number between 0 and the data size.
POTENTIAL_TYPE	A "character" string giving the potential of the model, i.e. "MOP" if the basis functions are polynomials, or "MTE" if they are exponentials.

Value

A "numeric" array which contains the new domain of the prior function.

Examples

## Data
X <- rnorm(15)

## Prior Data
priordata <- rnorm(5000)

## Learning
type = "MTE" 
fPrior <- univMoTBF(priordata, POTENTIAL_TYPE = type)

## New range
confident <- 5 ## confident <- 1,2,...,length(X)
domain <- range(X)
N <- length(X)
newRange <- newRangePriorData(fPrior, priorData = priordata, N = N,
domain = domain, s = confident, POTENTIAL_TYPE = type)
newRange

MoTBFs documentation built on April 18, 2022, 5:06 p.m.