Learn R Programming

⚠️There's a newer version (1.1) of this package.Take me there.

GMJMCMC

The GMJMCMC package provides functions to estimate Bayesian Generalized nonlinear models (BGNLMs) through a Genetically Modified Mode Jumping MCMC algorithm.

Installation and getting started

To install and load the package, just run

library(devtools)
install_github("jonlachmann/GMJMCMC", force=T, build_vignettes=T)
library(GMJMCMC)

With the package loaded, a vignette that shows how to run the package is available by running

vignette("GMJMCMC-guide")

Copy Link

Version

Install

install.packages('FBMS')

Monthly Downloads

274

Version

1.0

License

GPL-2

Maintainer

Jon Lachmann

Last Published

December 21st, 2023

Functions in FBMS (1.0)

mjmcmc.parallel

Run multiple mjmcmc runs in parallel, merging the results before returning.

gmjmcmc.parallel

Run multiple gmjmcmc (Genetically Modified MJMCMC) runs in parallel returning a list of all results.

pm05 polynomial term

pm1 polynomial term

gen.probs.mjmcmc

Generate a probability list for MJMCMC (Mode Jumping MCMC)

pm2 polynomial term

Main algorithm for GMJMCMC (Genetically Modified MJMCMC)

predict.gmjmcmc

Predict using a gmjmcmc result object.

negative ReLu function

p0p0 polynomial term

plot.mjmcmc_parallel

Plot a mjmcmc_parallel run

predict.gmjmcmc_parallel

Predict using a gmjmcmc result object from a parallel run.

string.population.models

Function to get a character respresentation of a list of models

Function to plot the results, works both for results from gmjmcmc and merged results from merge.results

p0p05 polynomial term

string.population

Function to get a character respresentation of a list of features

p0 polynomial term

predict.gmjmcmc_merged

Predict using a merged gmjmcmc result object.

Main algorithm for MJMCMC (Genetically Modified MJMCMC)

linear.g.prior.loglik

Log likelihood function for linear regression using Zellners g-prior

heavy side function

Function to print a quick summary of the results

logistic.loglik

Log likelihood function for logistic regression with a prior p(m)=sum(total_width) This function is created as an example of how to create an estimator that is used to calculate the marginal likelihood of a model.

summary.mjmcmc_parallel

Function to print a quick summary of the results

p2 polynomial term

Print method for "feature" class

p05 polynomial term

p3 polynomial term

Function to plot the results, works both for results from gmjmcmc and merged results from merge.results

To the 2.3 power function

Set the transformations option for GMJMCMC (Genetically Modified MJMCMC), this is also done when running the algorithm, but this function allows for it to be done manually.

plot.gmjmcmc_merged

Plot a gmjmcmc_merged run

Sigmoid function

logistic.loglik.alpha

Log likelihood function for logistic regression for alpha calculation This function is just the bare likelihood function

Function for calculating marginal inclusion probabilities of features given a list of models

Function to generate a function string for a model consisting of features

p0p1 polynomial term

To the 7/2 power function

Square root function

p0p2 polynomial term

Sine function for degrees

Predict using a mjmcmc result object.

predict.mjmcmc_parallel

Predict using a mjmcmc result object from a parallel run.

Cube root function

p0p3 polynomial term

p0pm05 polynomial term

p0pm1 polynomial terms

p0pm2 polynomial term

summary.gmjmcmc_merged

Function to print a quick summary of the results

summary.gmjmcmc

Function to print a quick summary of the results

tools:::Rd_package_title("FBMS")

Excerpt from the Open Exoplanet Catalogue data set

Cosine function for degrees

Plot convergence of best/median/mean/other summary log posteriors in time

Fit a BGNLM model using Genetically Modified Mode Jumping Markov Chain Monte Carlo (MCMC) sampling. Or Fit a BGLM model using Modified Mode Jumping Markov Chain Monte Carlo (MCMC) sampling.

Double exponential function

gen.probs.gmjmcmc

Generate a probability list for GMJMCMC (Genetically Modified MJMCMC)

Gaussian function

compute_effects

Compute effects for specified in labels covariates using a fitted model.

gen.params.mjmcmc

Generate a parameter list for MJMCMC (Mode Jumping MCMC)

gen.params.gmjmcmc

Generate a parameter list for GMJMCMC (Genetically Modified MJMCMC)

Negative GELU function

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).

negative heavy side function

Breast Cancer Wisconsin (Diagnostic) Data Set

gaussian.loglik

Log likelihood function for gaussian regression with a prior p(m)=r*sum(total_width).

gaussian.loglik.alpha

Log likelihood function for gaussian regression for alpha calculation This function is just the bare likelihood function Note that it only gives a proportional value and is equivalent to least squares