GAMLj: General Analyses for the Linear Model in Jamovi

General, Mixed and Generalized Models module for jamovi

GAMLj offers tools to estimate, visualize, and interpret General Linear Models, Mixed Linear Models and Generalized Linear Models with categorial and/or continuous variables, with options to facilitate estimation of interactions, simple slopes, simple effects, post-hoc tests, etc.

• ANOVA and Regression approach
• Continuous and categorical independent variables
• F-test and parameter estimates
• Confidence intervals
• Moderation made easy
• Simple slopes analysis
• Simple effects
• post-hocs analysis
• Plots for up to three-way interactions
• Automatic selection of best estimation methods and degrees of freedom selection
• Type III estimation

Available models are:

• OLS Regression (GLM)
• OLS ANOVA (GLM)
• OLS ANCOVA (GLM)
• Random coefficients regression (Mixed)
• Random coefficients ANOVA-ANCOVA (Mixed)
• Logistic regression (GZLM)
• Logistic ANOVA-like model (GZLM)
• Probit regression (GZLM)
• Probit ANOVA-like model (GZLM)
• Multinomial regression (GZLM)
• Multinomial ANOVA-like model (GZLM)
• Poisson regression (GZLM)
• Poisson ANOVA-like model (GZLM)
• Overdispersed Poisson regression (GZLM)
• Overdispersed Poisson ANOVA-like model (GZLM)
• Negative binomial regression (GZLM)
• Negative binomail ANOVA-like model (GZLM)
• Random coefficients logistic model (GlmMixed)
• Random coefficients poisson model (GlmMixed)

Please visit the sub-modules pages for details:

• GAMLj GLM
• GAMLj Mixed models
• GAMLj Generalized Models
• GAMLj Generalized Mixed Models

Examples

Some worked out examples of the analyses carried out with jamovi GAMLj are posted here (more to come)

General Linear Model

• GLM: Multiple regression, moderated regression, and simple slopes
• Polynomial Effects

Mixed models

• Mixed Models: Random coefficients regression
• Mixed Models: Repeated measures designs
• Mixed Models: Subjects by Stimuli random effects

Generalized Linear Models

• Multinomial analysis in jamovi
• Logistic analysis in jamovi
• Frequencies analysis with overdispersed models

Generalized Mixed Models

• Logistic mixed model in jamovi
• Poisson and Negative Binomial mixed models in jamovi

Details

• Details: GLM effect size indices
• Details: Model goodness of fit in GAMLj
• Generalized mixed model module technical details
• Generalized linear model module technical details
• Mixed Models module technical details
• Post-hocs Details

Installation

If you didn’t already, install jamovi and run it. Select the jamovi modules library and install GAMLj from there

Linux

If you are working in linux you can download Jamovi, then download the .jmo from this link gamlj binaries.

Others

You will first need to download Jamovi.

You can clone this repository and compile the module within R with

library(jmvtools)

jmvtools::install()

Install in R

To install it in R, simple use:

devtools::install_github("gamlj/gamlj")

Troubleshooting

Please check out the troubleshooting page.

Release notes

Please check out the Release notes.

Quality control

Please check the rosetta store for alignment with other software results and possible discrepancies.

Specs

• Details: GLM effect size indices
• Details: Model goodness of fit in GAMLj
• Generalized mixed model module technical details
• Generalized linear model module technical details
• Mixed Models module technical details
• Post-hocs Details

Comments?

Got comments, issues or spotted a bug? Please open an issue on GAMLj at github or send me an email