printConditional: Summary of conditional MoTBF densities

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

Summary of conditional MoTBF densities

Description

Print the description of an MoTBF demnsity for one variable conditional on another variable.

Usage

printConditional(conditionalFunction)

Arguments

conditionalFunction

the output of the function conditionalMethod. A list with the interval of the parent and the final "motbf" density function fitted in each interval.

Value

The results of the conditional function are shown.

See Also

conditionalMethod

Examples

## Data
X <- rexp(500)
Y <- rnorm(500, mean=X)
data <- data.frame(X=X,Y=Y)
cov(data)

## Conditional Learning
parent <- "X"
child <- "Y"
intervals <- 5
potential <- "MOP"
P <- conditionalMethod(data, nameParents=parent, nameChild=child, 
numIntervals=intervals, POTENTIAL_TYPE=potential)
printConditional(P)

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