The trend argument in estimate_slopes()
was renamed into slope. trend will remain as
alias.
New function collapse_by_group(), which extracts the
raw data points and “averages” (i.e. “collapses”) the response variable
over the levels of the grouping factor given in
collapse_by. Only works with mixed models. Additionally,
the plot() and visualization_recipe() methods
get a collapse_group argument to use this feature when
adding data points to plots using show_data or
show_residuals.
Fixed issue in estimate_slope() when
p_adjust = "esarey".
Fixed issue in estimate_contrasts() when
p_adjust = "tukey".
Fixed error with truncated message when standard errors could not be calculated.
The type argument in
estimate_grouplevel() gains a "marginal"
option, to return marginal group-levels estimates.
Added an as.data.frame() method for
modelbased objects.
Better formatting of the output for equivalence-tests, when the
equivalence argument was used. Related docs were added. It
is also possible to use parameters::equivalence_test() on
modelbased objects.
Function calls are now saved as call attribute in
modelbased objects.
More informative warnings and error messages were added to
estimate_contrasts() when computing effect sizes.
Improved plotting methods for the tinyplot package. The related vignette was also updated.
Methods for the tinyplot package were added.
estimate_slopes() now also gets the
estimate argument, to specify how to estimate / marginalize
over non-focal terms.
Improvements to estimate_contrasts():
comparison = "inequality" now also works when
contrasting slopes for a numeric predictor.
New option comparison = "inequality_ratio" for
estimate_contrast(), which computes the ratio of marginal
effects inequality measures across groups.
For contrasts, the message about the units of contrasts (“in %-points”) has been removed, because this was slightly misleading. The units were in %-points if multiplied by 100, but this multiplication was not done in the output.
estimate_contrasts() for slopes now allows
additional grouping of contrasts using the by argument
together with the comparison argument by specifying the
grouping variable in the formula,
e.g. contrast = c("x", "group") and
~ pairwise | group.
estimate_expectation() and
estimate_relation() now support objects of class
htest.
estimate_grouplevel() now supports models from
package coxme.
New function residualize_over_grid(), which
residualizes a model over a grid of predictors. This is useful to
visualize the residuals of a model over a grid of predictors.
visualisation_recipe() and plot() get a
show_residuals argument, to show the residuals of the
model, related to the data grid, in the plot.
Documentation of the display() method for
modelbased objects has been added.
Improved documentation and improved informative messages.
Message about unreliable standard errors (for certain models, when predicting random effects) was removed for now, as it is uncertain whether the standard errors were unreliable.
Modified code base to address changes in the marginaleffects package from version 0.29.0 onwards.
Fixed issue with by in
estimate_contrasts() when comparison was
"inequality".
Some comparison options, like "helmert" or
"poly", could not be specified as string-value, only as
formula. This has been fixed, so they can now be specified as
string-value, too.
In-formula transformations of predictors in by could
not be handled when by was not specified. This has been
fixed, and automatic detection of by variables now also
works with in-formula transformations.
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 (“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
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