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 ("levels") 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
"none" (default), "emmeans", or "bootES" (#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.
parameters
package update.NEWS.md file to track changes to the package