General Linear Model module of the GAMLj suite for jamovi
GAMLj version ≥ 3.0.0
The module estimates a general linear model with categorical and/or continuous variables, with options to facilitate estimation of interactions, simple slopes, simple effects, etc.
The module can estimate OLS linear models for any combination of categorical and continuous variables, thus providing an easy way for multiple regression, ANOVA, ANCOVA and moderation analysis.
The module provides ANOVA tables and parameters estimates for any estimated model. Variancebased effect size indices (eta, partial eta, partial omega, partial epsilon, and beta) and mean comparisons (Cohen’s d) are optionally computed.
Variables definition follows jamovi standards, with categorical independent variables defined in Factors and continuous independent variables in Covariates.
Effect size indices are optionally computed by selecting the following options (see Details: GLM effect size indices):
Confidence intervals of the parameters can be also selected in Options tab (see below).
Please check the details in Details: GLM effect size indices
The complete set of options for this panel is:
Effect Size 
The effect size to show in tables. They can be: "eta" for
etasquared, partial eta' for partial etasquared,
'omega' for omegasquared, 'omega partial' for
partial omegasquared, 'epsilon' for epsilonsquared,
'epsilon partial' for partial epsilonsquared and
'beta' for standardized coefficients (betas). Default is
"beta" and "eta partial" .

Estimates C.I.  Non standardized coefficients (estimastes) CI in tables if flagged 
β C.I.  Standardized coefficients CI in tables if flagged 
Confidence level  a number between 50 and 99.9 (default: 95) specifying the confidence interval width for the plots. 
Do not run  If flagged, the results are not updated each time an option is changed. This allows settings complex model options without waiting for the results to update every time. Unflag it when ready to go. 
By default, the model terms are filled in automatically for main effects and for interactions with categorical variables.
Interactions between continuous variables or categorical and continuous variables can be set by clicking the second arrow icon.
Polinomial effects for continuous variables can be added to the model. When a variable is selected in the Components field, a little number appears on the right side of the selection. The number indicates the order of the effect.
By increasing that number before dragging the term into the Model Terms field, one can include any high order effect. Increasing the order number and combining the selection with other variables allows including interactions involving higher order effects of a variable.
The option Intercept includes an intercept in the model. Unflagging it estimates zerointercept models (Regression through the origin, but see here before you do it ).
When Activate is flagged, models comparison options become visible.
Two models will be estimated and compared. The current model defined
in Model Terms
and the model defined in the
Nested Model
field. By default, the
Nested Model
terms are empty, so an interceptonly model is
compared with the current. When the user defines nested terms, the
comparison is updated.
Consider the following example:
The current model is composed by three main effects (x
,
z
and w
) and two interactions w*z
and w*z
. The nested model terms are only composed by the
main effects (x
, z
and w
). Thus,
the loglikelihood ratio test that it is performed to compare the model
will test the significance of the two interactions together. The output
offers a Table in which each model fit indices and tests are presented,
and the two models comparison test is presented.
The other options are:
Intercept  Estimates fixed intercept if flagged 
Nested intercept 
TRUE (default) or FALSE , estimates fixed
intercept. Not needed if formula is used.

Activate  Activates models comparison 
Test 
Omnibus test of the model is based on Ftest (default) or loglikelihood
ration test LRT .

It allows to code the categorical variables according to different coding schemas. The coding schema applies to all parameters estimates. The default coding schema is simple, which is centered to zero and compares each means with the reference category mean. The reference category is the first appearing in the variable levels.
Note that all contrasts but dummy guarantee to be centered to zero (intercept being the grand mean), so when involved in interactions the other variables coefficients can be interpret as (main) average effects. If contrast dummy is set, the intercept and the effects of other variables in interactions are estimated for the first group of the categorical IV.
Contrasts definitions are provided in the estimates table. More detailed definitions of the comparisons operated by the contrasts can be obtained by selecting Show contrast definition table.
Differently to standard R naming system, contrasts variables are always named with the name of the factor and progressive numbers from 1 to K1, where K is the number of levels of the factor.
In reading the contrast labels, one should interpret the
(1,2,3)
code as meaning “the mean of the levels 1,2, and 3
pooled toghether”. If factor levels 1,2 and 3 are all levels of the
factor in the samples, (1,2,3)
is equivalent to “the mean
of the sample”. For example, for a three levels factor, a contrast
labeled 1(1,2,3)
means that the contrast is comparing the
mean of level 1 against the mean of the sample. For the same factor, a
contrast labeled 1(2,3)
indicates a comparison between
level 1 mean and the subsequent levels means pooled together.
More details and examples Rosetta store: contrasts.
Continuous variables can be Centered, standardized (zscores), logtransformed (Log) or used as they are (none). The default is centered because it makes our lives much easier when there are interactions in the model, and do not affect the B coefficients when there are none. Thus, if one is comparing results with other software that does not center the continuous variables, without interactions in the model one would find only a discrepancy in the intercept, because in GAMLj the intercept represents the expected value of the dependent variable for the average value of the independent variable. If one needs to unscale the variable, simply select none.
Covariates conditioning rules how the model is conditioned to different values of the continuous independent variables in the simple effects estimation and in the plots when there is an interaction in the model.
Mean+SD: means that the IV is conditioned to the \(mean\), to \(mean+k \cdot sd\), and to \(meank\cdot sd\), where \(k\) is ruled by the white field below the option. Default is 1 SD.
Percentile 50 +offset: means that the IV is conditioned to the \(median\), the \(median+k P\), and the \(mediank\cdot P\), where \(P\) is the offset of percentile one needs. Again, the \(P\) is ruled by the white field below the option. Default is 25%. The default conditions the model to:
\(50^{th}25^{th}=25^{th}\) percentile
\(50^{th}\) percentile
\(50^{th}+25^{th}=75^{th}\) percentile
The offset should be within 5 and 50.
Note that with either of these two options, one can estimate simple effects and plots for any value of the continuous IV.
Covariates labeling decides which label should be associated with the estimates and plots of simple effects as follows:
Labels produces strings of the form \(Mean \pm SD\).
Values uses the actual values of the variables, after scaling.
Labels+Values produces labels of the
form \(Mean \pm SD=XXXX\), where
XXXX
is the actual value.
Unscaled Values produces labels indicating the actual value (of the mean and sd) of the original variable scale. This can be useful, for instance, when the user needs the estimates to be obtained with centered variables (because there are interactions, for instance), but the plot of the effects is preferred in the original scales of the moderators.
Unscaled Values + Labels as the previous option, but add also the label “Mean” and “SD” to the orginal values.
The Scaling on option decides how the
scaling of the variables handle missing values: First, keep in mind that
the model will be estimated on complete cases, no matter how this option
is set. When there are missing values, however, one can scale each
variable only on the complete cases (the default), or scale
columnwise
. If columnwise
is selected, the
mean and standard deviation of each variable used to scale the scores
are computed with the available data of the variable, independently of
possible missing values in other variables.
The option Dependent variable scale allows to transform the dependent variable. The dependent variable can be centered, standardized (zscores), logtransformed (Log) or used as it is (none). The default is none, so no transformation is applied.
Posthoc tests can be accomplished for the categorical variables groups by selecting the appropriated factor and flag the required tests
Posthoc tests are implemented based on R package emmeans. All tecnical info can be found here
Along with the means comparisons, one can obtain also the Cohen’s \(d\) effect size indices. Different formulation of the Cohen’s \(d\) are available, and they differ in the way the pooled standard error is computed.
Cohen’s d (model SD) \(d_{mod}\): the means difference is divided by the estimated standard deviation computed based on the model residual variance.
Cohen’s d (sample SD) \(d_{sample}\): the means difference is divided by the pooled standard deviation computed within each group.
Hedge’s g \(g_{sample}\): the means difference is divided by the pooled standard deviation computed within each group, corrected for sample bias. The correction is the one describe by Hedges and Olkin (2014) based on the Gamma function.
The “plots” menu allows for plotting main effects and interactions for any combination of types of variables, making it easy to plot interaction means plots, simple slopes, and combinations of them. The best plot is chosen automatically.
By filling in Horizontal axis one obtains the group means of the selected factor or the regression line for the selected covariate.
By filling in Horizontal axis and Separated lines one obtains a different plot depending on the type of variables selected:
By filling in Separate plots one can
probe higherorder interactions. If the selected variable is a factor,
one obtains a twoway graph (as previously defined) for each level of
the “Separate plots” variable. If the selected variable is a covariate,
one obtains a twoway graph (as previously defined) for the
Separate plots
variable centered to conditioning values
selected in the Covariates conditioning
options. Any number of plots can be obtained depending on the order of
the interaction.
The remaining options are defined as follows:
Display 
'None' (default), Confidence Intervals , or
Standard Error . Display on plots no error bars, use
confidence intervals, or use standard errors on the plots, respectively.

Observed scores 
TRUE or FALSE (default), plot raw data along
the predicted values

Yaxis observed range 
TRUE or FALSE (default), set the Yaxis range
equal to the range of the observed values.

X original scale 
If selected, the Xaxis variable is scaled with the orginal scale of the
variable, independently to the scaling set is the
Covariates Scaling .

Varying line types  If selected, a black and white theme is set for the plot, with multiple lines (if present) drawn in different styles. 
Simple effects can be computed for any combination of types of variables, making it easy to probe interactions, simple slopes, and combinations of them. Simple effects can estimated up to any order of interaction. If only one moderator is set in the Moderators field, the effect of the variable in the Simple effects variable is computed at different levels of the moderator. If more than one moderator is defined, the effects are estimated for all combinations of the moderator levels.
Simple effects are computed following the same logic of the plots. They correspond to the plotted effects as defined above. As for plots, the effects are estimated for different levels of the categorical moderators and for the conditioning values of the continuous moderators defined in Covariates Scaling panel.
When there is more than one moderator, one can activate Simple interactions to obtain estimation and tests
for lower order interactions at different levels of a moderator. Simple
interactions are computed using the last variable appearing in the Moderators field as moderator. In the case
depicted in the figure above, the interaction w*x
is
estimated and tested at different levels of z
.
Print the estimate expected means, SE, df and confidence intervals of
the predicted dependent variable by factors in the model. Any
combination available in the model (main effects, interactions,
nonlinear terms), can be requested. If the term involves categorical
independent variables, means of each level of the variable are
presented. If the term involves continuous variables, expected means
computed at the levels defined in Covariate Scaling
are
presented.
Homogeneity tests  Provides Levene’s test for equal variances across groups defined by factors (homoschedasticity). 
Normality of residuals  Provides KolmogorovSmirnov and ShapiroWilk tests for normality of residuals. 
QQ plot of residuals  Outputs a QQ plot (observed residual quantiles on expected residual quantiles). More general info here 
Residuals Histogram  Outputs the histogram of the distribution of the residuals, with an overlaying ideal normal distribution with mean and variance equal to the residuals distribution parameters. 
ResidualsPredicted plot  Produces a scatterplot with the residuals on the Yaxis and the predicted in the Xaxis. It can be usufull to assess heteroschdasticity. 
Identify extremes  Indentify 1% and 99% extreme values in the plots by marking them with their rown number in the dataset 
CI Method 
The method to estimate the confidence intervals. Standard method
(Wald ) or bootstrap methods. For bootstrap,
Quantile computes the confidence intervals based on the
bootstrap distribution, whereas BCAi computes the bootstrap
bias corrected accelerated intervals.

Bootstrap rep.  The number bootstrap repetitions. 
SE Method 
The method to estimate the standard errors. Standard for
Wald’s method or Robust for robust method.

Method 
HC1 to HC3 sets the
heteroschedasticityconsistent robust standard error. See sandwich
R package for deatils.

On intercept  If selected provides ìnformation about the intercept (F test, effect size indexes) 
On Effect sizes  If selected, provides ìnformation about the effect size indexes 
Coefficients Covariances 
TRUE or FALSE (default), shows coefficients
covariances

Predicted  Saves in the dataset the predicted values of the model 
Residuals  Saves in the dataset the residual values of the model 
Remove notes  Removes all notes and warnings from the Tables. Useful to produce pubblication quality tables. 
Some worked out examples of the analyses carried out with jamovi GAMLj are posted here (more to come)
Some more information about the module specs can be found here # Specs
Got comments, issues or spotted a bug? Please open an issue on GAMLj at github or send me an email
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