Generalized Linear Models module of the GAMLj suite for jamovi
GAMLj version ≥ 1.6.0
The module estimates generalized linear models with categorial and/or continuous dependent and independent variables, with options to facilitate estimation ofinteractions, simple slopes, simple effects, etc.
The module can estimate several linear models:
Models are defined by a link function (LF) and the dependent variable distribution, thus allowing to model different types of dependent variables:
Linear model: identity LF, gaussian distribution, yielding a general linear model for continuous dependent variables.
Poisson model: logarithm LF, Poisson distribution, modelling count dependent variables. This model is often called log-linear model when the independent variables are all categorical.
Poisson (overdispersion) model: Overdispersed Poisson model: logarithm LF, Poisson distribution, quasi-maximum likelihood estimation, with overdispersion, modelling count dependent variables. This model is often used for overdispersed data.
Negative binomial model: logarithm LF, negative binomial distribution, maximum likelihood estimation, with overdispersion, modelling count dependent variables. This model is often used for overdispersed data.
Logistic model: logit LF, binomial distribution, modelling dichotomous dependent variables.
Probit model: inverse of the cumulative normal distribution link function, binomial distribution, modelling dichotomous dependent variables.
Ordinal model: s proportional odds logistic regression, cumlative logit LF, multinomial distribution, modelling ordered dependent variables.
Multinomial model: logit LF, multinomial distribution, modelling categorical dependent variables.
Custom model: combination of distribution family and link function.
The available distributions are:
The available link functions are:
The plausibility of the distribution/link function combination is no checked, but errors are issued if the data do not conform to the chosen custom model.
For each model, any combination of categorical and continuous variables can be set as independent variables, thus providing an easy way for multiple regression, ANOVA-like, ANCOVA-like and moderation analysis for categorical and count dependent variables. An Offset variable can be defined, which is included in the model with a fixed coefficient (set to 1) (see )
The options of this panel are:
Effect size indices.
Odd Rations (default) exponentiates
the coefficients. For dichotomous dependent variables
Relative Risk indices can be obtained.
Marginal Effects computes the marginal effects (if the
TRUE (default) or
FALSE , exp(B) CI in table
FALSE (default), coefficients CI in
|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 ones can be set by selecting one or more variables and 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.
When Activate is flagged, models comparison options become visible.
Two models will be estimated and compared. The current model defined
Model Terms and the model defined in the
Nested Model field. By default, the
Nested Model terms are empty, so an intercept-only 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 (
w) and two interactions
w*z. The nested model terms are only composed by the
main effects (
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 options are:
TRUE (default) or
FALSE, estimates fixed
intercept. Not needed if
formula is used.
TRUE (default) or
FALSE, estimates fixed
intercept. Not needed if
formula is used.
|Not present in R
|Predictors in precision model
|Include the predictors also for the precision phi (beta regression)
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 K-1, 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
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
1-(2,3) indicates a comparison between
level 1 mean and the subsequent levels means pooled together.
Continuous variables can be centered, standardized (z-scores), log-transformed (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 \(mean-k\cdot sd\), 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 \(median-k\cdot P\), 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:
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 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.
Post-hoc tests can be accomplished for the categorical variables groups by selecting the appropriated factor and flag the required tests
Post-hoc tests are implemented based on R package emmeans. All tecnical info can be found here
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 higher-order interactions. If the selected variable is a factor,
one obtains a two-way graph (as previously defined) for each level of
the “Separate plots” variable. If the selected variable is a covariate,
one obtains a two-way 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
Plots interpretation varies depending on the model being estimated. All plots are, however, depicting predicted values in the response original scale (usually probabilities). See details and interpretation discussion.
The options are:
|Error Bar Definition
Use no error bars, use confidence intervals, or use standard errors on
the plots, respectively.
Plot ordinal model predicted values in as probabilities
response) or predicted class (
FALSE (default), plot raw data along
the predicted values
|Y-axis observed range
FALSE (default), set the Y-axis range
equal to the range of the observed values.
|X original scale
FALSE (default), use original scale
|Varying line types
FALSE (default), use different
linetypes per levels.
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
estimated and tested at different levels of
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,
non-linear 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
The method used to compute the confidence intervals.
Standard uses the Wald method to compute standard errors
and confidence intervals.
Profile computes Profile
Likelihood Based Confidence Interval, in which the bounds are chosen
based on the percentiles of the chi-square distribution around the
maximum likelihood estimate.
Bootstrap percent performs a
non-parametric boostrap, with
Bootstrap rep repetitions,
and compute the CI based on the percentiles of the boostrap
BCa implements the bias-corrected
|a number bootstrap repetitions.
FALSE (default), shows coefficients
|Parallel lines test
|Test parallel lines assumptions in cumulative link model (ordinal regression)
Saves the predicted values of the model. Predicted values are always
scaled in the dependent variable original scale, that in the majority of
cases is the probability scale. For
Poisson models and
Negative Binomial the count scale is used.
|Saves the residual values of the model. The response scale is used.
|Remove all notes and warning from the Tables. Useful to produce pubblication quality tables.
Some worked out examples of the analyses carried out with jamovi GAMLj GZLM are posted here (more to come)