A brand new GAMLj · jamovi


GAMLj is a jamovi module designed to estimate a wide range of linear
models, including the general linear model (ANOVA/Regression), the
generalized linear model (logistic, multinomial, Poisson, etc.), and
random coefficients models (mixed and multilevel models) for both
continuous and categorical dependent variables. Since its initial
release, the module has undergone continuous updates to enhance its
effectiveness for students, teachers, and analysts. However, driven by
user feedback and the evolving requirements of modern statistical
modeling approaches, a major update became necessary. Thus, GAMLj3 was

General Improvements

The upgrade of GAMLj was guided by two main principles: the enrichment
of options and the promotion of consistency across models. In terms of
options, GAMLj3 has expanded its repertoire beyond the existing
functions. It now includes features such as bootstrap confidence
, robust standard errors, and a plethora of effect size
, including variance-based and standardized mean differences.
The new version also offers a range of tests and plots for assessing
model assumptions and adequacy.

Numerous options that were previously available in GAMLj have been
enhanced and generalized. For instance, plots of model effects were
initially restricted to three-way interactions, but they are now
unlimited in the number of moderators they can visualize. A similar
expansion has been applied to simple effects analysis, which is now
applicable to any number of moderators and can conduct simple
interaction analysis
. This enables the evaluation of interactions at
various levels of the moderators.

One brand new feature is the ability to conduct model comparisons.
Upon request, the user can specify a nested model for comparison with
the target model, facilitating tests on sets of predictors. Model
comparisons yield appropriate fit indices for both the target model and
the nested model, along with log-likelihood ratio tests to support
inferences from the comparison.

In terms of consistency, considerable effort has been invested in
offering the same options across all estimable models. The aim is to
allow users to employ the same techniques, including post-hoc tests,
simple effects, model comparisons, etc., with all types of models, be
they linear, generalized, or mixed. Both the graphical user interface
(GUI) and the output tables have been designed to maintain as much
uniformity as possible across these models, ensuring that users can
seamlessly transition from one model to another. Moreover, we have taken
care to keep the GUI as similar as possible to the previous version of
the module, so users already familiar with the module do not need to
learn new strategies to carry out their analyses.

To give a glance of these features and the philosophy that inspired
their implementation, we can check out the model comparison feature. In
the GLM, the usual interface welcomes the user setting the model

and the effects of the model.

When needed, the user can activate the Model Comparison option,
so the GUI updates to allow specifying the terms of a nested model being

When model comparison is active, a new table appears in the output with
the goodness of fit indices of the two models, and the inferential

Thanks to the consistency principle, the module offers the same GUI and
output layout for generalized linear models, mixed models, and
generalized mixed models. Nonetheless, users who do not need this
feature can interact with the module without additional fields or
options that might distract from their work. Worked-out examples can be

Specific Improvements

There are several new features worth checking out specific to each
available model. Here we mention the extension of the available options
within the generalized linear models.

The generalized linear model has been expanded to include the Beta
regression model
and the Ordinal regression (proportional odds
. These are applications that have been gaining popularity in
the scientific community. Notably, the range of random coefficients
generalized models has also been extended to encompass the mixed
effects multinomial model
and mixed effects ordinal model. Once
again, the objective is to provide users with access to a wide array of
models for both fixed effects and mixed effects applications.”

In terms of the linear mixed model, a significant feature that has been
implemented is the ability to specify the form of the residual
variance-covariance matrix
. Previously, GAMLj’s mixed model relied on
the R package ‘lme4’ (Bates et al. 2015) to estimate the
mixed model, which did not allow for defining a specific form of
covariance among residuals. In GAMLj3, both the ‘lme4’ (Bates et al.
) and ‘nlme’ packages (Pinheiro, Bates, and R Core Team
) are used, with the latter being utilized to shape the
residual variances-covariances matrix. This enhancement greatly improves
the application of the mixed model to repeated-measure designs, where
residuals are often autoregressive or exhibit more correlation than
expected. This feature simply entails to interact with the new panel
available in the Mixed Model GUI (Refer to this for a worked-out

As this example illustrates, the improvements in GAMLj3 have been made
possible thanks to the numerous R packages provided to the public by the
R community. Without the contributions of these packages, GAMLj3 would
be meager. We’ve made an effort to cite these packages in the jamovi
output whenever possible.


GAMLj3 represents a complete rewrite of the original module. While we
have striven to maintain a user experience that is similar to previous
versions, it’s important to note that the internal architecture has
undergone significant changes. As a result, the new version of the
module is not compatible with analyses conducted using older GAMLj
versions (prior to 2.7). To ensure users have access to their previous
analyses created with GAMLj versions prior to 2.7, GAMLj3 will coexist,
at least for some time, with the older version in the jamovi library.
Furthermore, we believe a beta testing period will be instrumental in
ensuring its quality and robustness.


GAMLj help page is now updated to
provide help for the new version. We all know that writing man pages is
a long and time-consuming effort, but we hope that user can find support
regarding the major features of the module both in the GAMLj help
, the
, and in some annotated
discussion about the models being
. Those resorces
should be considered works in progress, hopefully helpful. And do not
forget to visit jamovi forum to ask
questions and open discussion about the software.


Bates, Douglas, Martin Mächler, Ben Bolker, and Steve Walker. 2015.
“Fitting Linear Mixed-Effects Models Using
lme4.” Journal of Statistical Software 67
(1): 1–48. https://doi.org/10.18637/jss.v067.i01.

Pinheiro, José, Douglas Bates, and R Core Team. 2023. Nlme: Linear and
Nonlinear Mixed Effects Models

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