lorad_estimate: Calculates the LoRaD estimate of the marginal likelihood

In lorad: Lowest Radial Distance Method of Marginal Likelihood Estimation

Calculates the LoRaD estimate of the marginal likelihood

Description

Provided with a data frame containing sampled paraneter vectors and a dictionary relating column names to parameter types, returns a named character vector containing the following quantities:

• logML (the estimated log marginal likelihood)

• nsamples (number of samples)

• nparams (length of each parameter vector)

• training_frac (fraction of samples used for training)

• tsamples (number of samples used for training)

• esamples (number of sampled used for etimation)

• coverage (nominal fraction of the estimation sampled used)

• esamplesused (number of estimation samples actually used for estimation)

• realized_coverage (actual fraction of estimation sample used)

• rmax (lowest radial distance: defines boundary of working parameter space)

• log_delta (volume under the unnormalized posterior inside working parameter space)

Usage

lorad_estimate(params, colspec, training_frac, training_mode, coverage)

Arguments

params	Data frame in which rows are sample points and columns are parameters, except that last column holds the log posterior kernel
colspec	Named character vector associating column names in params with column specifications
training_frac	Number between 0 and 1 specifying the training fraction
training_mode	One of random, left, or right, specifying how training fraction is chosen
coverage	Number between 0 and 1 specifying fraction of training sample used to compute working parameter space

Value

Named character vector of length 11.

Examples

normals <- rnorm(1000000,0,10)
prob_normals <- dnorm(normals,0,10,log=TRUE) 
proportions <- rbeta(1000000,1,2)
prob_proportions <- dbeta(proportions,1,2,log=TRUE)
lengths <- rgamma(1000000, 10, 1)
prob_lengths <- dgamma(lengths,10,1,log=TRUE)
paramsdf <- data.frame(
    normals,prob_normals,
    proportions, prob_proportions,
    lengths, prob_lengths)
columnkey <- c(
    "normals"="unconstrained", 
    "prob_normals"="posterior", 
    "proportions"="proportion", 
    "prob_proportions"="posterior", 
    "lengths"="positive", 
    "prob_lengths"="posterior")
results <- lorad_estimate(paramsdf, columnkey, 0.5, 'random', 0.1)
lorad_summary(results)

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