learnMoTBFpriorInformation {MoTBFs} | R Documentation |
Description
Learns a function using prior information.
Usage
learnMoTBFpriorInformation(
priorData,
data,
s,
POTENTIAL_TYPE,
domain = range(data),
coeffversion = 4,
restrictDomain = TRUE,
maxParam = NULL
)
Arguments
priorData |
A "numeric" array which contains the values of the variable we have information apriori about.
|
data |
A "numeric" array which contains the values to fit.
|
s |
A "numeric" coefficient which fixes the confidence of the prior knowledge
we are going to introduce. By default it is NULL , only we must modify it if we want
to incorporate prior information to the fits.
|
POTENTIAL_TYPE |
A "character" string specifying the posibles potential
types, must be one of "MOP" or "MTE" .
|
domain |
A "numeric" array which contains the limits to defined the data function.
By default it is the range of the data.
|
coeffversion |
A "numeric" value between 1--4 which contains the used version for computing the coefficients of 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 model.
|
restrictDomain |
This argument lets us choose if the domain is used joining both domains,
the prior one and the data domain or trimming them. By default TRUE is used, so
the domain will be trimmed.
|
maxParam |
A "numeric" value which indicates the maximum number of coefficients in the function. By default it is NULL ;
if not, the output is the function which gets the best BIC with at most this number of parameters.
|
Value
A list with the elements
coeffs |
An "numeric" array with the two coefficients of the linear opinion pool
|
posteriorFunction |
The final function after combining.
|
priorFunction |
The fit of the prior data.
|
dataFunction |
The fit of the original data.
|
rangeNewPriorData |
A "numeric" vector which contains the final domain where the functions are defined.
|
See Also
getCoefficients
Examples
## Data
X <- rnorm(15)
## Prior Data
priordata <- rnorm(5000)
## Test data
test <- rnorm(1000)
testData <- test[test>=min(X)&test<=max(X)]
## Learning
type <- "MOP"
confident <- 3 ## confident <- 1,2,...,length(X)
f <- learnMoTBFpriorInformation(priorData = priordata, data = X, s = confident,
POTENTIAL_TYPE = type)
attributes(f)
## Log-likelihood
sum(log(as.function(f$dataFunction)(testData)))
sum(log(as.function(f$posteriorFunction)(testData))) ## best loglikelihood
[Package
MoTBFs version 1.2
Index]