MoTBFs (version 1.2)

getCoefficients: Get the Coefficients

Description

Compute the coefficients for the linear opinion pool

Usage

getCoefficients(fPI, rangeNewPriorData, fD, data, domain, coeffversion)

Arguments

fPI

The fitted function to the prior data of class "motbf".

rangeNewPriorData

An array of length two with the new domain of the prior function.

fD

The fitted function to the original data of class "motbf".

data

A "numeric" array which contains the values to fit.

domain

A "numeric" array with the limits where defining the data function.

coeffversion

A "numeric" value between 1--4 which contains the used version for computing the coefficients in the linear opinion pool to combine the prior function and the data function. By default coeffversion = "4" is used, so the combination depends on the goodness of the model versus another random positive MoTBF model.

Value

A "numeric" value of length 2 giving the coefficients which are the weigth of the two function to combine.

Details

coeffversion can be: "1" coef1 and coef2 are the sum of the probabilities of one of the function over the sum of all probabilities, respectively; "2" coef1 and coef2 are the solution of a linear optimization problem which tries to maximize the sum 1 for each row of probabilities; "3" coef1 and coef2 are the difference of the log-likelihood of the evaluated model and a random uniform model over the sum of both differences, respectively; "4" coef1 and coef2 are the difference of the log-likelihood of the evaluated model and a ramdom positive MoTBF model over the sum of both differences, respectively.

See Also

learnMoTBFpriorInformation

Examples

# NOT RUN {
## Data
X <- rnorm(15)

## Prior Data
priordata <- rnorm(5000)

## Learning
confident <- 5
type <- "MOP"
f <- learnMoTBFpriorInformation(priorData = priordata, data = X, s = confident,
POTENTIAL_TYPE = type)
attributes(f)
 
## Coefficients: linear opinion pool
getCoefficients(fPI = f$priorFunction, rangeNewPriorData = f$domain, fD = f$dataFunction, 
data = X, domain = range(X), coeffversion = 4)

getCoefficients(fPI = f$priorFunction, rangeNewPriorData = f$domain, fD = f$dataFunction, 
data = X, domain = range(X), coeffversion = 1)

getCoefficients(fPI = f$priorFunction, rangeNewPriorData = f$domain, fD = f$dataFunction, 
data = X, domain = range(X), coeffversion = 3)

getCoefficients(fPI = f$priorFunction, rangeNewPriorData = f$domain, fD = f$dataFunction, 
data = X, domain = range(X), coeffversion = 2)

# }