Package: selectMeta 1.0.8
selectMeta: Estimation of Weight Functions in Meta Analysis
Publication bias, the fact that studies identified for inclusion in a meta analysis do not represent all studies on the topic of interest, is commonly recognized as a threat to the validity of the results of a meta analysis. One way to explicitly model publication bias is via selection models or weighted probability distributions. In this package we provide implementations of several parametric and nonparametric weight functions. The novelty in Rufibach (2011) is the proposal of a non-increasing variant of the nonparametric weight function of Dear & Begg (1992). The new approach potentially offers more insight in the selection process than other methods, but is more flexible than parametric approaches. To maximize the log-likelihood function proposed by Dear & Begg (1992) under a monotonicity constraint we use a differential evolution algorithm proposed by Ardia et al (2010a, b) and implemented in Mullen et al (2009). In addition, we offer a method to compute a confidence interval for the overall effect size theta, adjusted for selection bias as well as a function that computes the simulation-based p-value to assess the null hypothesis of no selection as described in Rufibach (2011, Section 6).
Authors:
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selectMeta.pdf |selectMeta.html✨
selectMeta/json (API)
NEWS
| # Install 'selectMeta' in R: |
| install.packages('selectMeta', repos = c('https://numbersman77.r-universe.dev', 'https://cloud.r-project.org')) |
- education - Dataset open vs. traditional education on creativity
- passive_smoking - Dataset on the effect of environmental tobacco smoke
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 9 years agofrom:d7ef53f74f. Checks:OK: 5 NOTE: 2. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Sep 02 2024 |
| R-4.5-win | NOTE | Sep 02 2024 |
| R-4.5-linux | NOTE | Sep 02 2024 |
| R-4.4-win | OK | Sep 02 2024 |
| R-4.4-mac | OK | Sep 02 2024 |
| R-4.3-win | OK | Sep 02 2024 |
| R-4.3-mac | OK | Sep 02 2024 |
Exports:DearBeggDearBeggLoglikDearBeggMonotoneDearBeggMonotoneCIthetaDearBeggMonotonePvalSelectionDearBeggProfileLLDearBeggToMinimizeDearBeggToMinimizeProfiledPvaleffectBiasHijIyenGreenLoglikTIyenGreenMLEIyenGreenWeightnormalizeTpPoolpPvalqPvalrPvalweightLine
Dependencies:DEoptim
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Estimation of Weight Functions in Meta Analysis | selectMeta-package selectMeta |
| Compute the nonparametric weight function from Dear and Begg (1992) | DearBegg DearBeggLoglik DearBeggMonotone DearBeggToMinimize Hij |
| Compute an approximate profile likelihood ratio confidence interval for effect estimate | DearBeggMonotoneCItheta DearBeggProfileLL DearBeggToMinimizeProfile |
| Compute simulation-based p-value to assess null hypothesis of no selection | DearBeggMonotonePvalSelection |
| Dataset open vs. traditional education on creativity | education |
| Compute bias for each effect size based on estimated weight function | effectBias |
| Compute MLE and weight functions of Iyengar and Greenhouse (1988) | IyenGreen IyenGreenLoglikT IyenGreenMLE IyenGreenWeight normalizeT |
| Dataset on the effect of environmental tobacco smoke | passive_smoking |
| Pool p-values in pairs | pPool |
| Functions for the distribution of p-values | dPval pPval Pval qPval rPval |
| Function to plot estimated weight functions | weightLine |
