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abcrf (version 1.9)

Approximate Bayesian Computation via Random Forests

Description

Performs Approximate Bayesian Computation (ABC) model choice and parameter inference via random forests. Pudlo P., Marin J.-M., Estoup A., Cornuet J.-M., Gautier M. and Robert C. P. (2016) . Estoup A., Raynal L., Verdu P. and Marin J.-M. . Raynal L., Marin J.-M., Pudlo P., Ribatet M., Robert C. P. and Estoup A. (2019) .

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Version

Install

install.packages('abcrf')

Monthly Downloads

637

Version

1.9

License

GPL (>= 2)

Maintainer

JeanMichel Marin

Last Published

August 9th, 2022

Functions in abcrf (1.9)

Plot the posterior density given a new summary statistic

Calculate and plot for different numbers of tree, the out-of-bag errors associated with an ABC-RF object

predict.regAbcrf

Predict posterior expectation, median, variance and quantiles given a new dataset using a reg-ABC-RF object

Predict out-of-bag posterior expectation, median, variance, quantiles and error measures using a reg-ABC-RF object

Calculate and plot for different numbers of tree, the out-of-bag mean squared errors associated with a REG-ABC-RF object

Plot of a reg-ABC-RF object

Predict posterior covariance between two parameters for new data using two reg-ABC-RF objects

Create an ABC-RF object: a classification random forest from a reference table towards performing an ABC model choice

A simulated example in population genetics

variableImpPlot

Variable importance plot from a random forest

Predict and evaluate the posterior probability of the MAP for new data using an ABC-RF object

Read a reference table simulated from DIYABC

Create a reg-ABC-RF object: a regression random forest from a reference table aimed out predicting posterior expectation, variance and quantiles for a parameter

Plot of an ABC-RF object