Package: metagam 0.4.0.9000
Oystein Sorensen
metagam: Meta-Analysis of Generalized Additive Models
Meta-analysis of generalized additive models and generalized additive mixed models. A typical use case is when data cannot be shared across locations, and an overall meta-analytic fit is sought. 'metagam' provides functionality for removing individual participant data from models computed using the 'mgcv' and 'gamm4' packages such that the model objects can be shared without exposing individual data. Furthermore, methods for meta-analysing these fits are provided. The implemented methods are described in Sorensen et al. (2020), <doi:10.1016/j.neuroimage.2020.117416>, extending previous works by Schwartz and Zanobetti (2000) and Crippa et al. (2018) <doi:10.6000/1929-6029.2018.07.02.1>.
Authors:
metagam_0.4.0.9000.tar.gz
metagam_0.4.0.9000.zip(r-4.5)metagam_0.4.0.9000.zip(r-4.4)metagam_0.4.0.9000.zip(r-4.3)
metagam_0.4.0.9000.tgz(r-4.4-any)metagam_0.4.0.9000.tgz(r-4.3-any)
metagam_0.4.0.9000.tar.gz(r-4.5-noble)metagam_0.4.0.9000.tar.gz(r-4.4-noble)
metagam_0.4.0.9000.tgz(r-4.4-emscripten)metagam_0.4.0.9000.tgz(r-4.3-emscripten)
metagam.pdf |metagam.html✨
metagam/json (API)
| # Install 'metagam' in R: |
| install.packages('metagam', repos = c('https://lifebrain.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/lifebrain/metagam/issues
Pkgdown site:https://lifebrain.github.io
Last updated 7 months agofrom:60f4f988a1. Checks:7 OK. Indexed: yes.
| Target | Result | Latest binary |
|---|---|---|
| Doc / Vignettes | OK | Dec 30 2024 |
| R-4.5-win | OK | Dec 30 2024 |
| R-4.5-linux | OK | Dec 30 2024 |
| R-4.4-win | OK | Dec 30 2024 |
| R-4.4-mac | OK | Dec 30 2024 |
| R-4.3-win | OK | Dec 30 2024 |
| R-4.3-mac | OK | Dec 30 2024 |
Exports:getmasdmetagamplot_between_study_sdplot_dominanceplot_heterogeneitystrip_rawdata
Dependencies:clicolorspacefansifarverggplot2gluegtableisobandlabelinglatticelifecyclemagrittrMASSmathjaxrMatrixmetadatmetaformgcvmunsellnlmenumDerivpbapplypillarpkgconfigR6RColorBrewerrlangscalestibbleutf8vctrsviridisLitewithr
Dominance Plots
Rendered fromdominance.Rmdusingknitr::rmarkdownon Dec 30 2024.Last update: 2022-01-14
Started: 2020-01-30
Heterogeneity Plots
Rendered fromheterogeneity.Rmdusingknitr::rmarkdownon Dec 30 2024.Last update: 2022-01-14
Started: 2020-01-30
Introduction
Rendered fromintroduction.Rmdusingknitr::rmarkdownon Dec 30 2024.Last update: 2022-01-18
Started: 2020-01-30
Multivariate Smooth Terms
Rendered frommultivariate.Rmdusingknitr::rmarkdownon Dec 30 2024.Last update: 2022-01-14
Started: 2020-02-05
Simultaneous confidence intervals and p-values
Rendered frompvals.Rmdusingknitr::rmarkdownon Dec 30 2024.Last update: 2022-01-16
Started: 2021-11-12
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| metagam: Meta-analysis of generalized additive models. | metagam-package |
| Meta-analysis of generalized additive models | metagam |
| Plot between-study standard deviation | plot_between_study_sd |
| Dominance plot | plot_dominance |
| Heterogeneity Plot | plot_heterogeneity |
| Plot estimated smooth terms | plot.metagam |
| Print method for metagam objects. | print.metagam |
| Print method for striprawdata | print.striprawdata |
| Print output from summary of metagam fit. | print.summary.metagam |
| Strip rawdata from a generalized additive model | strip_rawdata strip_rawdata.bam strip_rawdata.gam strip_rawdata.gamm strip_rawdata.list |
| Summary method for metagam objects | summary.metagam |
| Summary method for GAMs stripped for rawdata | summary.striprawdata |
