The comparison argument gets two new option, "inequality" and "inequality_pairwise", to compute the marginal effects inequality measure, which summarizes the the overall effect of categorical predictors or the comprehensive effect of a predictor across all outcome categories of a nominal or ordinal dependent variable.
Added docs to show how to use modelbased with finite mixture models from package brms.
Improved support for finite mixture models (currently only the mixture() family for model from package brms are supported).
Improved printing for joint-tests with backend = "emmeans".
Improved handling when p-adjustment methods that are only available in the emmeans package were used for the marginaleffects backend.
The column header for the predicted values in estimate_means() for multinomial models from packages nnet and brglm2 has been changed to Probability, to better reflect the scale of the predictions.
New vignettes (Case Studies) about using modelbased with finite mixture models and interrupted time series analysis.
The p_adjust argument gets a new option, "sup-t", to calculate simultaneous confidence intervals.
Added a display() method for modelbased objects.
Fixed printing and plotting for models from packages nnet and brglm2.
Fixed issues with object of class aov.
Fixed issue with the plot() method for estimate_slopes() for Bayesian models.
estimate_contrasts() for results from estimate_relation() and alike is now more efficient for larger number of contrasts.
Updated information of citation(). If you want to cite the modelbased package, please use the JOSS publication as reference (https://joss.theoj.org/papers/10.21105/joss.07969).
estimate_contrasts() for results from estimate_relation().The comparison argument can now also be a custom function, or a matrix (e.g., to define contrasts).
The comparison argument can now also be "joint", to jointly test hypotheses (i.e. conducting a joint test) in factorial designs.
New vignette about user-defined contrasts and joint tests in estimate_contrasts().
pool_slopes(), to pool results from estimate_slopes() applied to imputed data.reshape_grouplevel() now takes the correct number of specified random effects groups into account when reshaping results.In general, it is now possible to make estimate means, contrasts and slopes for distributional parameters for models from package brms using the predict argument.
estimate_grouplevel() gets arguments test, dispersion and diagnostic, that are internally passed to parameters::model_parameters(), but with different defaults.
estimate_prediction() and estimate_relation() now support Wiener-models (Drift Diffusion Models) from package brms.
estimate_prediction(), estimate_relation() and similar functions now include the Row column for models with ordinal or categorical response variables when the data argument was provided.
estimate_slopes() can now also calculate average marginal effects of a predictor, just for the trend of that predictor within a certain range of values.
estimate_slopes() gets a predict argument, to either select the scale of the estimates slopes, or to estimate slopes (marginal effects) for distributional parameters of brms models.
estimate_contrasts() gives an informative error message when arguments by and contrast have identical variables (which does not work).
Column names of predicted values for backend = "emmeans" has changed for models like logistic regression, or beta regression. Formerly, name was Mean, now it is Probability or Proportion, depending on the model.
Exposed iterations argument in estimate_prediction() and estimate_relation().
Option estimate = "average no longer prints information on averaged predictors in the footer, because strictly, the predictions are averaged over, and not the non-focal variables.
Better handling for models with offsets in estimate_means() and estimate_contrasts(). Informative messages are given when models include offset terms, and it is possible to fix the offset value using the offset argument. The offset argument is also available for estimate_relation(), estimate_prediction() and similar.
For consistency, estimate_slopes() now also uses the residual degrees of freedom by default (like estimate_means()) when calculating confidence intervals and p-values.
Minor improvements to the documentation.
Fixed issues in estimate_grouplevel() for models from package rstanarm.
Fixed issues in calculating correct confidence intervals (and possibly p-values) for pooling functions pool_parameters() and pool_predictions().
Fixed issue in estimate_means() for multivariate response models from package brms.
Fixed issue with wrong y-axis label for plots from estimate_slopes().
Fixed issue with weights in estimate_relation().
Fixed issue in printed output for the statistic column, which should be z for the marginaleffects backend, when argument df = Inf.
The deprecated function visualisation_matrix() has been removed. Use insight::get_datagrid() instead.
The "average" option for argument estimate was renamed into "typical". The former "average" option is still available, but now returns marginal means fully averaged across the sample.
The transform argument now also works for estimate_slopes() and for estimate_contrasts() with numeric focal terms.
estimate_contrasts() no longer calls estimate_slopes() for numeric focal terms when these are integers with only few values. In this case, it is assumed that contrasts of values (âlevelsâ) are desired, because integer variables with only two to five unique values are factor-alike.
estimate_contrasts: now supports optional standardized effect sizes, one of ânoneâ (default), âemmeansâ, or âbootESâ (#227, @rempsyc).
The predict() argument for estimate_means() gets an "inverse_link" option, to calculate predictions on the link-scale and back-transform them to the response scale after aggregation by groups.
estimate_means(), estimate_slopes() and estimate_contrasts() get a keep_iterations argument, to keep all posterior draws from Bayesian models added as columns to the output.
New functions pool_predictions() and pool_contrasts(), to deal with modelbased objects that were applied to imputed data sets. E.g., functions like estimate_means() can be run on several data sets where missing values were imputed, and the multiple results from estimate_means() can be pooled using pool_predictions().
The print() method is now explicitly documented and gets some new options to customize the output for tables.
estimate_grouplevel() gets a new option, type = "total", to return the sum of fixed and random effects (similar to what coef() returns for (Bayesian) mixed models).
New option "esarey" for the p_adjust argument. The "esarey" option is specifically for the case of Johnson-Neyman intervals, i.e. when calling estimate_slopes() with two numeric predictors in an interaction term.
print_html() and print_md() pass ... to format-methods (e.g. to insight::format_table()), to tweak the output.
The show_data argument in plot() is automatically set to FALSE when the models has a transformed response variable, but predictions were not back-transformed using the transform argument.
The plot() method gets a numeric_as_discrete argument, to decide whether numeric predictors should be treated as factor or continuous, based on the of unique values in numeric predictors.
Plots now use a probability scale for the y-axis for models whose response scale are probabilities (e.g., logistic regression).
Improved printing for estimate_contrasts() when one of the focal predictors was numeric.
Fixed issue in the summary() method for estimate_slopes().
Fixed issues with multivariate response models.
Fixed issues with plotting ordinal or multinomial models.
Fixed issues with ci argument, which was ignored for Bayesian models.
Fixed issues with contrasting slopes when backend was "emmeans".
Fixed issues in estimate_contrasts() when filtering numeric values in by.
Fixed issues in estimate_grouplevel().
Fixed issue in estimate_slopes() for models from package lme4.
The default package used for estimate_means(), estimate_slopes() and estimate_contrasts() is now marginaleffects. You can set your preferred package as backend using either the backend argument, or in general by setting options(modelbased_backend = "marginaleffects") or options(modelbased_backend = "emmeans").
Deprecated argument and function names have been removed.
Argument fixed has been removed, as you can fix predictor at certain values using the by argument.
Argument transform is no longer used to determine the scale of the predictions. Please use predict instead.
Argument transform is now used to (back-) transform predictions and confidence intervals.
Argument method in estimate_contrasts() was renamed into comparison.
All model_*() alias names have been removed. Use the related get_*() functions instead.
The show_data argument in plot() defaults to FALSE.
The "marginaleffects" backend is now fully implemented and no longer work-in-progress. You can set your preferred package as backend using either the backend argument, or in general by setting options(modelbased_backend = "marginaleffects") or options(modelbased_backend = "emmeans").
All estimate_*() functions get a predict argument, which can be used to modulate the type of transformation applied to the predictions (i.e. whether predictions should be on the response scale, link scale, etc.). It can also be used to predict auxiliary (distributional) parameters.
estimate_means() and estimate_contrasts() get a estimate argument, to specify how to estimate over non-focal terms. This results in slightly different predicted values, each approach answering a different question.
estimate_contrasts() gains a backend argument. This defaults to "marginaleffects", but can be set to "emmeans" to use features of that package to estimate contrasts and pairwise comparisons.
estimate_expectation() and related functions also get a by argument, as alternative to create a datagrid for the data argument.
Many functions get a verbose argument, to silence warnings and messages.
estimate_contrasts() did not calculate contrasts for all levels when the predictor of interest was converted to a factor inside the model formula.
Fixed issue in estimate_contrasts() when comparsison (formerly: method) was not "pairwise".
3.6.Fixed issues with printing-methods.
Maintenance release to fix failing tests in CRAN checks.
visualisation_matrix() has now become an alias (alternative name) for the get_datagrid() function, which is implemented in the insight package.API changes: levels in estimate_contrasts has been replaced by contrast. levels and modulate are in general aggregated under at.
estimate_prediction() deprecated in favour of estimate_response().
estimate_expectation() now has data=NULL by default.
General overhaul of the package.
Entire refactoring of visualisation_matrix().
Option of standardizing/unstandardizing predictions, contrasts and means is now available via standardize() instead of via options.
Introduction of model_emmeans() as a wrapper to easily create emmeans objects.
estimate_smooth() transformed into describe_nonlinear() and made more explicit.
estimate_link() now does not transform predictions on the response scale for GLMs. To keep the previous behaviour, use the new estimate_relation() instead. This follows a change in how predictions are made internally (which now relies on get_predicted(), so more details can be found there).Predicted is now the name of the predicted column for Bayesian models (similarly to Frequentist ones), instead of the centrality index (e.g., Median).estimate_slope() now gives an informative error when no numeric predictor is present.Partial support of formulas.
Refactor the emmeans wrapping.
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