merge_results {FBMS}

Merge a list of multiple results from many runs This function will weight the features based on the best mlik in that population and merge the results together, simplifying by merging equivalent features (having high correlation).

Description

Merge a list of multiple results from many runs This function will weight the features based on the best mlik in that population and merge the results together, simplifying by merging equivalent features (having high correlation).

Usage

merge_results(
  results,
  populations = NULL,
  complex.measure = NULL,
  tol = NULL,
  data = NULL
)

Arguments

results

A list containing multiple results from GMJMCMC (Genetically Modified MJMCMC).

populations

Which populations should be merged from the results, can be "all", "last" (default) or "best".

complex.measure

The complex measure to use when finding the simplest equivalent feature, 1=total width, 2=operation count and 3=depth.

tol

The tolerance to use for the correlation when finding equivalent features, default is 0.

data

Data to use when comparing features, default is NULL meaning that mock data will be generated, if data is supplied it should be of the same form as is required by gmjmcmc, i.e. with both x, y and an intercept.

Value

An object of class "gmjmcmc_merged" containing the following elements:

features

The features where equivalent features are represented in their simplest form.

marg.probs

Importance of features.

counts

Counts of how many versions that were present of each feature.

results

Results as they were passed to the function.

pop.best

The population in the results which contained the model with the highest log marginal posterior.

thread.best

The thread in the results which contained the model with the highest log marginal posterior.

crit.best

The highest log marginal posterior for any model in the results.

reported

The highest log marginal likelihood for the reported populations as defined in the populations argument.

rep.pop

The index of the population which contains reported.

best.log.posteriors

A matrix where the first column contains the population indices and the second column contains the model with the highest log marginal posterior within that population.

rep.thread

The index of the thread which contains reported.

result <- gmjmcmc.parallel( runs = 1, cores = 1, list(populations = "best", complex.measure = 2, tol = 0.0000001), matrix(rnorm(600), 100), P = 2, gaussian.loglik, loglik.alpha = gaussian.loglik.alpha, c("p0", "exp_dbl") )

summary(result)

plot(result)

merge_results(result$results)