Calculate P(|R-R'|>\(\beta\)), the probability of ratio deviation for a dataframe and given CKM parameter and for each \(\beta\) value.

Calculate P(|R-R'|>\(\beta\)), the probability of ratio deviation for a dataframe and given CKM parameter and for each \(\beta\) value.

Usage

estimate_proba_precision_statistic_df(
  data,
  fun = function(a, b) {
a/b * 100
 },
  D,
  V,
  js = 0,
  betas = c(0, 1, 2, 5, 10, 20),
  posterior = FALSE,
  parallel = FALSE,
  max_cores = NULL
)

Arguments

data

data.frame with two columns corresponding to the numerators and denominators of the ratios de chaque ratio

fun

Function. Statistic calculation (default: a/b*100)

D

Integer. Maximum deviation

V

Numeric. Noise variance

js

Integer. Sensitivity threshold

betas

Numeric vector. Precision thresholds to evaluate

posterior

Logical. Use posterior approach? (default: FALSE)

parallel

Boolean, whether the calculation should be parallelized

max_cores,

integer, maximum number of jobs to run in parallel

Value

a dataframe

Details

If posterior=FALSE, the calculation is based on the a priori approach, that is, the provided ratio (A/B) is the original/real ratio. Otherwise, the calculation is based on the a posteriori approach, that is, the provided ratio (A/B) is the ratio resulting from CKM perturbation. #' The output dataframe contains the following columns: - beta: precision threshold - A: numerator - B: denominator - R: ratio = A/B*100 - proba: P(|R-R'|>\(\beta\)) for the given \(\beta\)

Examples

if (FALSE) { # \dontrun{
test <- data.frame(
  A = sample(1:50, 10, replace = TRUE),
  B = sample(50:1000, 10, replace = TRUE)
)

fun = \(a,b){a/b * 100}
D = 10
V = 10
js = 4

# A priori approach (given R the original ratio)
res <- estimate_proba_precision_statistic_df(test, fun, D, V)

# A posteriori approach (given R the perturbed ratio)
res_ap <- estimate_proba_precision_statistic_df(test, fun, D, V, posterior = TRUE)
} # }