lorad_standardize: Transforms unconstrained parameters to have the same location...

In lorad: Lowest Radial Distance Method of Marginal Likelihood Estimation

Transforms unconstrained parameters to have the same location and scale

Description

Standardizes parameters that have already been transformed (if necessary) to have unconstrained support. Standardization involves subtracting the sample mean and dividing by the sample standard deviation. Assumes that the log posterior kernel (i.e. the log of the unnormalized posterior) is the last column in the supplied data frame.

Usage

lorad_standardize(df, coverage)

Arguments

df	Data frame containing a column for each model parameter sampled and a final column of log posterior kernel values
coverage	Fraction of the training sample used to compute working parameter space

Value

List containing the log-Jacobian of the standardization transformation, the inverse square root matrix, a vector of column means, and rmax (radial distance to furthest point in working parameter space)

lorad documentation built on May 29, 2024, 1:30 a.m.