calculate_divergence {emcAdr} | R Documentation |
Calculate the divergence between 2 distributions (the true Distribution and the learned one)
Description
Calculate the divergence between 2 distributions (the true Distribution and the learned one)
Usage
calculate_divergence(
empirical_distribution,
true_distribution,
method = "TV",
Filtered = FALSE
)
Arguments
empirical_distribution |
A numeric vector of values representing the empirical distribution (return value of DistributionAproximation function) |
true_distribution |
A numeric vector of values representing the true distribution computed by the trueDistributionSizeTwoCocktail function |
method |
A string, either "TV" or "KL" to respectively use the total variation distance or the Kullback-Leibler divergence. (default = "TV") |
Filtered |
Should we use the filtered distribution or the normal one |
Value
A numeric value representing the divergence of the 2 distributions
Examples
data("ATC_Tree_UpperBound_2024")
data("FAERS_myopathy")
estimated_score_distribution = DistributionApproximation(epochs = 10,
ATCtree = ATC_Tree_UpperBound_2024,
observations = FAERS_myopathy[1:100,], Smax =2)
true_score_distribution = trueDistributionSizeTwoCocktail(ATCtree = ATC_Tree_UpperBound_2024,
observations = FAERS_myopathy[1:100,], beta = 4)
divergence <- calculate_divergence(empirical_distribution = estimated_score_distribution,
true_distribution = true_score_distribution)
[Package emcAdr version 1.2 Index]