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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
packageThese binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.