mpsnorm: Marginal Publication Selection Meta-analysis Model

In publipha: Bayesian Meta-Analysis with Publications Bias and P-Hacking

Marginal Publication Selection Meta-analysis Model

Description

Density, distribution, and random variate generation for the marginalized distribution of the publication selection meta-analysis model

Usage

dmpsnorm(x, theta0, tau, sigma, alpha = c(0, 0.025, 0.05, 1), eta, log = FALSE)

pmpsnorm(
  q,
  theta0,
  tau,
  sigma,
  alpha = c(0, 0.025, 0.05, 1),
  eta,
  lower.tail = TRUE,
  log.p = FALSE
)

rmpsnorm(n, theta0, tau, sigma, alpha = c(0, 0.025, 0.05, 1), eta)

Arguments

x, q	vector of quantiles.
theta0	vector of means.
tau	vector of heterogeneity parameters.
sigma	vector of study standard deviations.
alpha	vector of thresholds for publication bias.
eta	vector of publication probabilities, normalized to sum to 1.
log, log.p	logical; If TRUE, probabilities are given as log(p).
lower.tail	logical; If TRUE (default), the probabilities are P[X\leq x] otherwise, P[X\geq x].
n	number of observations. If length(n) > 1, the length is taken to be the number required.

Details

These functions assume a normal underlying effect size distribution and one-sided selection on the effects. For the fixed effects publication bias model see psnorm.

Value

dmpsnorm gives the density, pmpsnorm gives the distribution function, and rmpsnorm generates random deviates.

References

Hedges, Larry V. "Modeling publication selection effects in meta-analysis." Statistical Science (1992): 246-255.

Moss, Jonas and De Bin, Riccardo. "Modelling publication bias and p-hacking" Forthcoming (2019)

Examples

rmpsnorm(100, theta0 = 0, tau = 0.1, sigma = 0.1, eta = c(1, 0.5, 0.1))

publipha documentation built on April 4, 2023, 5:19 p.m.