Package: powerNLSEM 0.1.2
Julien Patrick Irmer
powerNLSEM: Simulation-Based Power Estimation (MSPE) for Nonlinear SEM
Model-implied simulation-based power estimation (MSPE) for nonlinear (and linear) SEM, path analysis and regression analysis. A theoretical framework is used to approximate the relation between power and sample size for given type I error rates and effect sizes. The package offers an adaptive search algorithm to find the optimal N for given effect sizes and type I error rates. Plots can be used to visualize the power relation to N for different parameters of interest (POI). Theoretical justifications are given in Irmer et al. (2024a) <doi:10.31219/osf.io/pe5bj> and detailed description are given in Irmer et al. (2024b) <doi:10.3758/s13428-024-02476-3>.
Authors:
powerNLSEM_0.1.2.tar.gz
powerNLSEM_0.1.2.zip(r-4.5)powerNLSEM_0.1.2.zip(r-4.4)powerNLSEM_0.1.2.zip(r-4.3)
powerNLSEM_0.1.2.tgz(r-4.4-any)powerNLSEM_0.1.2.tgz(r-4.3-any)
powerNLSEM_0.1.2.tar.gz(r-4.5-noble)powerNLSEM_0.1.2.tar.gz(r-4.4-noble)
powerNLSEM_0.1.2.tgz(r-4.4-emscripten)powerNLSEM_0.1.2.tgz(r-4.3-emscripten)
powerNLSEM.pdf |powerNLSEM.html✨
powerNLSEM/json (API)
NEWS
| # Install 'powerNLSEM' in R: |
| install.packages('powerNLSEM', repos = c('https://jpirmer.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/jpirmer/powernlsem/issues
Last updated 2 months agofrom:d2cef36c77. Checks:OK: 1 NOTE: 6. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Oct 27 2024 |
| R-4.5-win | NOTE | Oct 27 2024 |
| R-4.5-linux | NOTE | Oct 27 2024 |
| R-4.4-win | NOTE | Oct 27 2024 |
| R-4.4-mac | NOTE | Oct 27 2024 |
| R-4.3-win | NOTE | Oct 27 2024 |
| R-4.3-mac | NOTE | Oct 27 2024 |
Exports:FSRLMSpower_searchpowerNLSEMreanalyse.powerNLSEMsimulateNLSEMSRUPI
Dependencies:clicolorspacecrayonfansifarverggplot2gluegtableisobandlabelinglatticelavaanlifecyclemagrittrMASSMatrixmgcvmnormtmunsellmvtnormnlmenumDerivpbapplypbivnormpillarpkgconfigquadprogR6RColorBrewerrlangscalesstringistringrtibbleutf8vctrsviridisLitewithr
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Factor Score Regression approach | FSR |
| Latent moderated strctural equations by Klein and Moosbrugger (2000), the ML approach to nonlinear SEM | LMS |
| plot powerNLSEM object | plot.powerNLSEM |
| Search function to find N for desired power | power_search |
| powerNLSEM function | powerNLSEM |
| print powerNLSEM objects | print.powerNLSEM |
| print summary for powerNLSEM objects | print.summary.powerNLSEM |
| Reanalyse powerNLSEM object | reanalyse.powerNLSEM |
| simulate data from lavModel object | simulateNLSEM |
| Scale Regression approach | SR |
| Summary function for powerNLSEM objects | summary.powerNLSEM |
| Unconstrained Product Indicator approach by Marsh et al. (2004), with extensions by Kelava and Brandt (2009) | UPI |