lorad_standardize {lorad} | R Documentation |
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)
[Package lorad version 0.0.1.0 Index]