PAMLj power analysis for linear models in jamovi

Version 0.7.*


Estimation power parameters (required sample size, expected power, minimal detectable effect size and required alpha) for General Linear Models.

  • Correlation
  • Regression
  • T-Tests (independent, paired and one sample, and equivalence tests)
  • Proportions (independent, paired, one sample)
  • ANOVA
  • Partial eta-squared based analysis
  • Eta-squared and R2R^2 based analysis
  • Standardized coefficients based analysis
  • F-test for factorial designs from partial eta-squared (between, within and mixed)
  • F-test for factorial designs from cells means and sd (between, within and mixed)
  • Mediation with Sobel, joint significance and Monte Carlo tests
  • Structural Equations Models
  • Mixed Models (multilevel models)

Docs and help

More informations can be found at PAMLj page

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="")