emcAdr (version 1.3)

DistributionApproximation: The MCMC method that runs the random walk on a single cocktail in order to estimate the distribution of score among cocktails of size Smax.

Description

The MCMC method that runs the random walk on a single cocktail in order to estimate the distribution of score among cocktails of size Smax.

Usage

DistributionApproximation(
  epochs,
  ATCtree,
  observations,
  temperature = 1L,
  nbResults = 5L,
  Smax = 2L,
  p_type1 = 0.01,
  beta = 4L,
  max_score = 500L,
  num_thread = 1L,
  verbose = FALSE
)

Value

I no problem, return a List containing : - ScoreDistribution : the distribution of the score as an array with each cells representing the number of risks = (index-1)/ 10 - Outstanding_score : An array of the score greater than max_score, - Best_cocktails : the nbResults bests cocktails encountered during the run. - Best_scores : Score corresponding to the bestCocktails. - Filtered_score_distribution : Distribution containing score for cocktails taken by at least beta patients. - Best_cocktails_beta : the nbResults bests cocktails taken by at least beta patients encountered during the run. - Best_scores_beta : Score corresponding to the bestCocktailsBeta. - cocktailSize : Smax parameter used during the run. ; Otherwise the list is empty

Arguments

epochs

: number of steps for the MCMC algorithm

ATCtree

: ATC tree with upper bound of the DFS (without the root, also see on the github repo for an example)

observations

: real observation of the AE based on the medications of each patients (a DataFrame containing the medication on the first column and the ADR (boolean) on the second)

temperature

: starting temperature, default = 1 (denoted T in the article)

nbResults

: Number of returned solution (Cocktail of size Smax with the best oberved score during the run), 5 by default

Smax

: Size of the cocktail we approximate the distribution from

p_type1

: probability to operate type1 mutation. Note : the probability to operate the type 2 mutation is then 1 - P_type1. P_type1 must be in [0;1]. Default is .01

beta

: filter the minimum number of patients that must have taken the cocktail for his risk to be taken into account in the DistributionScoreBeta default is 4

max_score

: maximum number the score can take. Score greater than this one would be added to the distribution as the value max_score. Default is 500

num_thread

: Number of thread to run in parallel if openMP is available, 1 by default

verbose

: Output summary (default is false)

Examples

# \donttest{
data("ATC_Tree_UpperBound_2024")
data("FAERS_myopathy")

estimation = DistributionApproximation(epochs = 10, ATCtree = ATC_Tree_UpperBound_2024,
            observations = FAERS_myopathy)
# }