PAMLj
Power analysis for linear models in jamovi
0.2.0
Estimation of power parameters (required sample size, posthoc power, minimal detectable effect size and required alpha) for General Linear Models and other commonly used statistics.
- Correlation
- Proportions
- Regression
- t-test
- ANOVA
- Partial eta-squared based analysis
- Eta-squared and \(R^2\) based analysis
- Standardized coefficients based analysis
- Factorial designs (between, within and mixed)
- Mediation analysis, simple and complex
Main help pages
Please select the help page for a specific analysisValidation
Here we compare the results of PAMLj with other software that performs power analysis.- Correlation
- Factorial designs: power from means and SD
- Factorial designs: power from effect size
- GLM: required sample size
- GLM: posthoc power
- Mediation: Complex models
- Mediation: Simple models
- Independent Samples Proportions
- Paired Samples Proportions
- SEM
- Independent sample T-test: Equivalence tests
- Independent Samples t-test
- One Sample t-test
- Pairted Samples t-test
Consistency
Here we check some of the options of the module to demonstrate that they hopefully work (or do not).Details
Some more information about the module specs can be found here
Install in jamovi
Please install jamovi and run it. Select the jamovi modules library and install PAMLj from there
From GitHub
In your R script (or Rstudio) simply issue
library(jmvtools)
devtools::install_github("pamlj/pamlj")
From source
You will first need to download jamovi.
You can clone this repository and compile the module within R with
library(jmvtools)
jmvtools::install()
Programmatic name
paste(paste(LETTERS[c(16,1,13,12)],collapse =""),paste(letters[10]),sep="")
’
Comments?
Got comments, issues or spotted a bug? Please open an issue on PAMLj at github or send me an email