MoTBFs_Learning {MoTBFs} | R Documentation |
Learning MoTBFs in a Network
Description
Learn mixtures of truncated basis functions in a full hybrid network.
Usage
MoTBFs_Learning(
graph,
data,
numIntervals,
POTENTIAL_TYPE,
maxParam = NULL,
s = NULL,
priorData = NULL
)
Arguments
graph |
A network of the class |
data |
A datase of class |
numIntervals |
A |
POTENTIAL_TYPE |
A |
maxParam |
A |
s |
A |
priorData |
Prior dataset with values of the variables we have information apriori about.
This dataset must be of |
Details
If the variable is discrete then it computes the probabilities and the size of each leaf.
Value
A list of lists. Each list contains two elements
Child |
A |
functions |
A list with three elements: the name of the parents, a |
See Also
Examples
## Dataset Ecoli
require(MoTBFs)
data(ecoli)
data <- ecoli[,-c(1)] ## remove variable sequence
## Directed acyclic graph
dag <- LearningHC(data)
## Learning BN
intervals <- 3
potential <- "MOP"
P1 <- MoTBFs_Learning(graph = dag, data = data, numIntervals = intervals, POTENTIAL_TYPE=potential,
maxParam = 5)
printBN(P1)
## Learning BN
intervals <- 4
potential <- "MTE"
P2 <- MoTBFs_Learning(graph = dag, data = data, numIntervals = intervals, POTENTIAL_TYPE=potential,
maxParam = 15)
printBN(P2)