psnorm: Publication Selection Meta-analysis Model

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

Publication Selection Meta-analysis Model

Description

Density, distribution, quantile, random variate generation, and expectation calculation for the distribution for the publication selection meta-analysis model

Usage

dpsnorm(x, theta, sigma, alpha = c(0, 0.025, 0.05, 1), eta, log = FALSE)

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

rpsnorm(n, theta, sigma, alpha = c(0, 0.025, 0.05, 1), eta)

Arguments

x, q	vector of quantiles.
theta	vector of means.
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

The effect size distribution for the publication selection model is not normal, but has itself been selected for. These functions assume one-sided selection on the effects. These functions do not assume the existence of an underlying effect size distribution. For these, see mpsnorm.

Value

dpsnorm gives the density, ppsnorm gives the distribution function, and rpsnorm 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

rpsnorm(100, theta = 0, sigma = 0.1, eta = c(1, 0.5, 0.1))

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