fitPTLmodel: Calibrate Polynomial-Tail Laplace (PTL) model prdictions for...
In LCA: Localised Co-Dependency Analysis

Description Usage Arguments Details Value Author(s)

Fits PTL models to randomly sampled pairs of the dataset, to enable prediction of PTL model parameter values based on hyperparameter d.

1	fitPTLmodel(x,nPairs=10000)

`x`	Numeric data input array, standardised to range (0,1)
`nPairs`	Numeric value specifying the number of samplings of pairs of objects to use to obtain hyperparameter fits

Evaluates parameters for PTL model fits to the distributions of feature-wise differences between each of a specified (large) number of pairs of objects represented in dataset x. Obtains subsequent model fits explaining the individual PTL parameters alpha,beta,gamma in terms of the global (Euclidean) distances between the corresponding pairs of objects.

List with the following components:

`alpha`	Object of class `lm`, which can be used to predict an appropriate value of `alpha` in the PTL distribution corresponding to a pair of objects in the dataset with a specified global dissimilarity
`beta`	Object of class `lm`, which can be used to predict an appropriate value of `alpha` in the PTL distribution corresponding to a pair of objects in the dataset with a specified global dissimilarity
`gamma`	Object of class `lm`, which can be used to predict an appropriate value of `alpha` in the PTL distribution corresponding to a pair of objects in the dataset with a specified global dissimilarity