Censor meta-analytic dataset based on a multivariate step-function model

A functional that takes in a single step value, a weight representing the selection probability for the upper interval of p-values, and a dependence parameter, and returns a function that can be used to censor meta-analytic datasets according to a multivariate step-function model.

Usage

step_count_fun(cut_val = 0.025, weight = 1, psi = 0, renormalize = TRUE)

Arguments

cut_val

numeric value specifying the specifying the thresholds (or steps) where the selection probability changes.

weight

numeric value specifying the selection probability for the upper interval of p-values, i.e., for p-values larger than cut_val for an effect reported in a study with no p-values smaller than cut_val.

psi

numeric value controlling the degree of dependence in selection probabilities.

renormalize

logical indicating whether to normalize the step function to have a maximum value of 1, with a default value of TRUE.

Value

A function that can be used to censor a meta-analytic dataset based on a multivariate step-function model.