conditionalmotbf.learning: Learning conditional MoTBF densities

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

Learning conditional MoTBF densities

Description

Collection of functions used for learning conditional MoTBFs, computing the internal BIC, selecting the parents that get the best BIC value, and other internal functions required to learn the conditional densities.

Usage

conditionalMethod(
  data,
  nameParents,
  nameChild,
  numIntervals,
  POTENTIAL_TYPE,
  maxParam = NULL,
  s = NULL,
  priorData = NULL
)

conditional(
  data,
  nameParents,
  nameChild,
  domainChild,
  domainParents,
  numIntervals,
  mm,
  POTENTIAL_TYPE,
  maxParam = NULL,
  s = NULL,
  priorData = NULL
)

select(
  data,
  nameParents,
  nameChild,
  domainChild,
  domainParents,
  numIntervals,
  POTENTIAL_TYPE,
  maxParam = NULL,
  s = NULL,
  priorData = NULL
)

learn.tree.Intervals(
  data,
  nameParents,
  nameChild,
  domainParents,
  domainChild,
  numIntervals,
  POTENTIAL_TYPE,
  maxParam = NULL,
  s = NULL,
  priorData = NULL
)

BICscoreMoTBF(conditionalfunction, data, nameParents, nameChild)

Arguments

data	An object of class "data.frame".
nameParents	A "character" vector containing the names of the parent variables.
nameChild	A "character" string containing the name of the child variable.
numIntervals	A positive integer indicating the maximum number of intervals for splitting the domain of the parent variables.
POTENTIAL_TYPE	A "character" string, either MOP or MTE, corresponding to the type of basis function.
maxParam	A positive integer which indicates the maximum number of coefficients in the function. If specified, the output is the function which gets the best BIC with, at most, this number of parameters. By default, it is set to NULL.
s	A "numeric" value indicating the expert's confidence in the prior knowledge. This argument takes values on the interval [0, N], where N is the sample size, and is used to synchronize the support of the prior knowledge and the sample. By default, it is NULL, and must be modified only if prior information is to be incorporated in the learning process.
priorData	An object of class "data.frame", corresponding to the prior information.
domainChild	A "numeric" vector with the range of the child variable.
domainParents	An object of class "matrix" with the range of the parent variables, or a "numeric" vector if there is only one parent.
mm	One of the inputs and the output of the recursive internal function "conditional".
conditionalfunction	The output of the internal function learn.tree.Intervals.

Details

The main function, conditionalMethod(), fits truncated basis functions for the conditioned variable for each configuration of splits of the parent variables. The domain of the parent variables is splitted in different intervals and univariate functions are fitted in these ranges. The remaining above described functions are internal to the main function.

Value

The main function conditionalMethod returns a list with the name of the parents, the different intervals and the fitted densities

See Also

printConditional

Examples

## Dataset
X <- rnorm(1000)
Y <- rbeta(1000, shape1 = abs(X)/2, shape2 = abs(X)/2)
Z <- rnorm(1000, mean = Y)
data <- data.frame(X = X, Y = Y, Z = Z)

## Conditional Method
parents <- c("X","Y")
child <- "Z"
intervals <- 2

potential <- "MTE"
fMTE <- conditionalMethod(data, nameParents = parents, nameChild = child, 
numIntervals = intervals, POTENTIAL_TYPE = potential)
printConditional(fMTE)

##############################################################################

potential <- "MOP"
fMOP <- conditionalMethod(data, nameParents = parents, nameChild = child,
numIntervals = intervals, POTENTIAL_TYPE = potential, maxParam = 15)
printConditional(fMOP)

##############################################################################

##############################################################################
## Internal functions: Not needed to run #####################################
##############################################################################

domainP <- range(data[,parents])
domainC <- range(data[, child])
t <- conditional(data, nameParents = parents, nameChild = child,
domainParents = domainP, domainChild = domainC, numIntervals = intervals,
mm = NULL, POTENTIAL_TYPE = potential)
printConditional(t)
selection <- select(data, nameParents = parents, nameChild = child,
domainParents = domainP, domainChild = domainC, numIntervals = intervals,
POTENTIAL_TYPE = potential)
parent1 <- selection$parent; parent1
domainParent1 <- range(data[,parent1])
treeParent1 <- learn.tree.Intervals(data, nameParents = parent1,
nameChild = child, domainParents = domainParent1, domainChild = domainC,
numIntervals = intervals, POTENTIAL_TYPE = potential)
BICscoreMoTBF(treeParent1, data, nameParents = parent1, nameChild = child)

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