summary.motbf: Summarize an '"motbf"' object by describing its main...

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

Summarize an "motbf" object by describing its main features.

Description

Summarize an "motbf" object by describing its main features.

Usage

## S3 method for class 'motbf'
summary(object, ...)

## S3 method for class 'motbf_fit_cv'
summary(object, ...)

## S3 method for class 'univmotbf'
summary(object, ...)

## S3 method for class 'summary.univmotbf'
print(x, ...)

## S3 method for class 'piecewisemop'
summary(object, ...)

## S3 method for class 'summary.piecewisemop'
print(x, ...)

## S3 method for class 'jointmotbf'
summary(object, ...)

## S3 method for class 'summary.jointmotbf'
print(x, ...)

Arguments

object	An object of class "motbf".
...	further arguments passed to or from other methods.
x	An object of class "summary.motbf".

Value

The summary of an "motbf" object. It contains a list of elements with the most important information of the object.

See Also

univMoTBF

Examples

## Subclass 'MOP'
X <- rnorm(1000)
P <- univMoTBF(X, POTENTIAL_TYPE="MOP") ## or POTENTIAL_TYPE="MTE"
summary(P)
attributes(sP <- summary(P))
attributes(sP)
sP$Function
sP$Subclass
sP$Iterations

## Subclass 'MTE'
X <- rnorm(1000)
P <- univMoTBF(X, POTENTIAL_TYPE="MTE")
summary(P)
attributes(sP <- summary(P))
attributes(sP)
sP$Function
sP$Subclass
sP$Iterations

MoTBFs documentation built on Oct. 6, 2026, 1:06 a.m.