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Recent Releases of ggeffects

ggeffects - ggeffects 2.2.0

Major changes

  • The functions test_predictions() and jonhson_neyman() have been largely revised, due to breaking changes in the marginaleffects package. The two functions will now internally call modelbased::estimate_contrasts() and modelbased::estimate_slopes(), to reduce maintanance load. Thus, some features have changed or are probably no longer / not yet available.

Scientific Software - Peer-reviewed - R
Published by strengejacke over 1 year ago

ggeffects - ggeffects 2.1.0

Changes

  • The terms argument can now include up to five focal terms (formerly: four). Accordingly, the plot() method was revised. For four focal terms, a facet grid is used to plot all relevant panels. For five focal terms, multiple plots with facet grids are arranged using the patchwork package.

  • values_at() gains a new "threenum" option.

  • The test argument is test_predictions() can now also be a formula to calculate consecutive contrasts, contrasts against the reference level or against the "average" factor level.

Scientific Software - Peer-reviewed - R
Published by strengejacke over 1 year ago

ggeffects - ggeffects 2.0.0

Breaking changes

  • The way how type = "random" works has been revised. type = "random" no longer returns predictions intervals. Instead, use interval = "prediction". type = "random" is now mainly responsible for unit-level predictions in mixed models, as opposed to type = "fixed", which should be used for population-level predictions. The separation from type = "random" and the interval argument makes the handling for mixed models easier, more intuitive and consistent. Accordingly, the vignette regarding the introduction into mixed models with ggeffects has been largely revised.

  • The vcov_fun and vcov_type argument were removed and are now replaced by the single vcov argument, to be in line with the handling of heteroscedasticity-consistent standard errors in other packages (mainly: easystats eco-system).

  • The deprecated arguments for ggpredict(), vcov(), ggeffect() and ggemmeans() have been removed.

  • The deprecated arguments for the plot() method have been removed.

  • Options type = "random" and type = "zi_random" are not longer available for ggemmeans(). These were only responsible to set prediction intervals, which can be done with interval = "prediction" now.

Changes

  • Added a get_preditions() method, which can be used to implement own S3-classes to add support for new models to ggeffects. There is a corresponding vignette, too.

  • The plot() method gets a dot_shape argument, to change the shape of data points when show_data = TRUE.

  • test_predictions gains a test_args argument, to optionally pass further options to test for emmeans engine/options.

  • vcov() returns a more informative warning, when the variance-covariance matrix could not be extracted due to problems in creating the model matrix (which prevents confidence intervals from being calculated).

  • Added Okabe-Ito color scale to the available color ggeffects-palettes.

  • For models of class survreg, argument type can also be "quantile".

Bug fixes

  • Fixed issue with argument condition for ggaverage().

  • Fixed issues with missing confidence intervals for objects of class averaging.

Scientific Software - Peer-reviewed - R
Published by strengejacke over 1 year ago

ggeffects - ggeffects 1.7.2

Breaking changes

  • The deprecated argument ppd was removed.

  • Some of the deprecated arguments in plot() were removed.

  • Pooling functions now use the model's degrees of freedom to calculate the critical values for the confidence intervals.

Changes

  • test = "slope" (or test = "trend") are aliases in test_predictions() for test = NULL with numeric predictors.

  • predict_response() (and ggpredict(), ggemmeans() and ggeffect()) get an argument bias_correction, to correct for bias when back-transforming predictions for non-Gaussian mixed models.

Scientific Software - Peer-reviewed - R
Published by strengejacke over 1 year ago

ggeffects - ggeffects 1.7.1

General

  • Added support for models of class glm_weightit, ordinal_weightit, multinom_weightit from package WeightIt.

Bug fixes

  • Fixed issues for multivariate response models due changes in the last insight package updates.

  • Fixed issue with swapped lower and higher confidence interval values for models with inverse-link.

  • Fixed CRAN check issues due to breaking changes in the last marginaleffects update.

Scientific Software - Peer-reviewed - R
Published by strengejacke over 1 year ago

ggeffects - ggeffects 1.7.0

Breaking

  • The deprecated argument ci.lvl in test_predictions() was removed.

General

  • ggpredict() now supports models of class glmgee (package glmtoolbox).

  • ggemmeans() gains arguments vcov_fun, vcov_type and vcov_args to specify the variance-covariance matrix for the marginal means, similar to what is already available in ggpredict() and ggaverage().

  • When test = "contrast", the engine is automatically set to "emmeans" in test_predictions().

  • ggaverage() (or predict_response(..., margin = "empirical")) now also supports following type options for zero-inflated models: "zi_prob", "zero_inflated" and "fixed".

  • Support for zero-inflated models was massively improved in test_predictions(), which, for instance, now also supports scale = "zprob" to calculate contrasts for the zero-inflation probability for zero-inflated models from glmmTMB or pscl. Furthermore, when predictions for zero-inflation probabilities were calculated using pr <- predict_response(..., type = "zi_prob"), corresponding contrasts will be calculated with test_predictions(pr) automatically. Additionally, other types for models with zero-inflation component ("zero_inflated", "fixed") are supported as well.

  • ggeffect() now warns the user about arguments that are supported by ggpredict() or ggemmeans(), but not by this function (e.g., vcov_fun).

  • Improved accuracy of standard errors for test_predictions(..., engine = "ggeffects").

  • The terms argument now also accepts the shortcut "percentile" (plus numeric value) to select a range of percentiles for continuous variables, e.g. terms = "x [percentile90]" to select a range of the 90% percentile.

Bug fixes

  • Fixed issue with brms models with monotonic effects in formula (mo()).

  • Fixed issue in vcov() for ggeffects objects, which could occur in rare situations when some of the predictors were character vectors.

  • Fixed issue with calculation of standard errors when one of the focal term was a character vector.

  • Fixed issue in plot() method with show_data = TRUE, where in certain situations the raw data points were not colored when groups were present.

  • Fixed issue in plot() with too many data points when collapsing random effects groups.

Scientific Software - Peer-reviewed - R
Published by strengejacke almost 2 years ago

ggeffects - ggeffects 1.6.0

General

  • ggpredict() now works for models of class clm2 from package ordinal, however, confidence intervals are not yet supported for these models.

  • ggeffect() now passes the latent argument for models with ordinal outcome down to effects::Effect(), to plot effects for ordinal models on the latent scale.

  • When argument test in test_predictions() is "interaction", "consecutive", or a data frame, emmeans is automatically used as backend, as this is the relevant package that supports these argument types.

  • format() (and hence, print()) for test_predictions() gains a combine_levels argument, to combine levels of the focal term in the output table.

  • The engine argument in test_predictions() can now also be "ggeffects". However, this is currently work-in-progress and offers muss less options as the default engine, "marginaleffects". It can be faster in some cases, though, and works for comparing predicted random effects in mixed models.

  • test_predictions() now automatically falls back to engines "emmeans" or "ggeffects" if the marginaleffects (or emmeans) package is not installed.

  • predict_response(), test_predictions() and ggpredict() will warn the user when all focal terms are only included as random effects in the model and no appropriate type or margin is specified. This is to avoid meaningless results.

  • plot() gets an n_rows argument, to define the number of rows for the panel alignment. This is useful when the number of facets is large and the default alignment is not optimal.

  • The ppd argument for Bayesian models will be superseded by the interval argument, i.e. ppd = TRUE is equivalent to interval = "prediction" (and ppd = FALSE is equivalent to interval = "confidence").

  • When back_transform = FALSE, and model has a transformed response variable, the plot() method for ggeffects objects now rescales the raw data points. This ensures that the raw data points are plotted on the same scale as the predicted values when show_data = TRUE.

  • Minor revisions of documentation and vignettes, to improve readability and clarity.

  • Several arguments have been deprecated and replaced by new argument names. A warning is printed when deprecated arguments are used. The deprecated arguments will be removed in a future release.

Bug fixes

  • Fixed issue in print() for ggeffect() and models with ordinal outcome, where one column was too much in the output.

  • Fixed issue in test_predictions() with wrong order of term labels when a focal term was a character vector.

  • Fixed issue in ggpredict() with wbm models from package panelr.

  • Fixed issue in ggemmeans() for glmmTMB models with zero-inflation, when terms included variables that were specified in the conditional, but not in the zero-inflation model formula.

  • Fixed issue in ggpredict() for Stan models (from packages rstanarm and brms) where the ci_level argument was not correctly recognized.

  • Fixed CRAN check issues due to latest marginaleffects update.

Scientific Software - Peer-reviewed - R
Published by strengejacke about 2 years ago

ggeffects - ggeffects 1.5.1

General

  • Overhaul of the documentation (again), to provide more clarity about the terminology "adjusted predictions", "marginal means" and "marginal effects", and how to calculate each of these quantities using the ggeffects package.

  • print_html() methods were updated to work with the latest release of tinytable.

  • New print_md() method, to print the output as markdown table. This is useful inside RMarkdown or Quarto documents, where the output can be directly included.

Scientific Software - Peer-reviewed - R
Published by strengejacke about 2 years ago

ggeffects - ggeffects 1.5.0

New functions

  • predict_response() as "generic" high-level function, which is a replacement for ggpredict(), ggemmeans() and ggaverage(). The new function is more clear about how the function marginalizes over non-focal terms. The margin argument can be used to specify how to marginalize over non-focal terms, i.e. which function internally is used to compute the marginal effects.

General

  • The documentation was revised, to provide more clarity about what the package functions do and how to decide, which function or method to calculate marginal effects is the most appropriate.

  • Improved calculation of prediction intervals for Poisson regression models.

  • Improved handling of the vcov_fun argument. This argument now accepts an estimation type as string, e.g. vcov_fun = "HC0", which is then used to compute the variance-covariance matrix. Thus, it is no longer necessary to define both vcov_fun and vcov_type, if the variance-covariance matrix is covered by one of the pre-defined estimation types. See ?ggpredict for details.

  • hypothesis_test() now also accepts the vcov_fun argument, and not only vcov. This ensures consistency with the vcov_fun argument in ggpredict(). Furthermore, the information about the type of variance-covariance matrix is saved to the ggeffects object returned by ggpredict(), predict_response() etc., and if this information is available, it is automatically used in hypothesis_test() when a ggeffects object is passed to the function.

Bug fixes

  • Fixed bug in wrong order of printed (sub-)tables for predictions.

  • Fixed wrong table column name for confidence interval columns for other confidence levels than the default 95% in print() for ggeffects objects.

  • Fixed issue with ggpredict() for models of class fixest when the cluster variable was numeric.

Scientific Software - Peer-reviewed - R
Published by strengejacke over 2 years ago

ggeffects - ggeffects 1.4.0

Breaking Changes

  • The print() method has been revised. A format() method was added, which allows to format the output of ggpredict() (and ggeffect() etc.) for printing. The refactoring of the print() method makes the code base easier to maintain and it is easier to enhance the print-functionality. Now it is possible to create HTML tables as well, using print_html(). The style of the output has also slightly changed. By default, confidence intervals are no longer enclosed in parentheses. You can change this behaviour by passing the ci_brackets argument to print() (see examples), or permanently define custom parentheses or brackets with, e.g., options(ggeffects_ci_brackets = c("[", "]")). Additionally, there are new arguments to further control the output of the tables: collapse_ci can be used to collapse confidence intervals into a single column together with the predicted values. collapse_tables can be used to collapse multiple tables into a single table (only applies when there is more than one focal term). Again, these settings can be permanently defined via options (see ?print.ggeffects for details).

New functions

  • print_html(), to print the output as HTML table. This method is available for objects from ggpredict() (and alike) as well as hypothesis_test().

General

  • A new vignette was added, showing examples for the new print-functionality.

Bug fixes

  • Fixed issue with ggpredict() for models of class vglm with multivariate responses.

Scientific Software - Peer-reviewed - R
Published by strengejacke over 2 years ago

ggeffects - ggeffects 1.3.4

General

  • ggpredict() now supports models of class rqs from package quantreg.

  • Fixed issues to be compatible with forthcoming update of emmeans.

Scientific Software - Peer-reviewed - R
Published by strengejacke over 2 years ago

ggeffects - ggeffects 1.3.3

New function

  • ggaverage(), to compute average predicted values. This function is based on marginaleffects::avg_predictons().

  • pool_comparisons(), to pool results from multiple calls to hypothesis_test(), e.g. with imputed data sets.

General

  • Support for sdmTMB (sdmTMB) models.

  • Improved support for the logistf package, including models flic() and flac().

  • Confidence intervals for predictions from merMod models (package lme4) now use the standard errors returned by predict(..., se.fit = TRUE). This should not affect numerical results, but can be more robust for certain edge cases. Note that standard errors are only based on predict() when tpye = "fixed". For type = "random", standard errors are still based on the model's variance-covariance matrix, taking uncertainty from random effects into account.

  • hypothesis_test() now suppports models from package parsnip.

  • johnson_neyman() gains a p_adjust argument, to adjust p-values for multiple comparisons. Currently, only p_adjust = "esarey" (resp. p_adjust = "es") and p_adjust = "fdr" (resp. p_adjust = "bh") are supported.

Bug fixes

  • ggpredict() now computes appropriate predicted probabilites for models of class rms::lrm() with ordinal outcome.

  • Fixed issue in ggpredict() for type = "random" when sampling from random effects levels, where the levels were numeric characters with a pattern like "001", "002", etc.

  • Fixed minor issue in plot.ggalleffects().

  • ... arguments in ggpredict() are now passed down to the predict() method for mgcv::gam() models.

Scientific Software - Peer-reviewed - R
Published by strengejacke over 2 years ago

ggeffects - ggeffects 1.3.2

Breaking changes

  • Some function arguments will be renamed, to achieve consistency across the package and across other packages where I'm involved in the development. This will be a soft transition, i.e. the old argument names will still work for some package updates.

Changes

  • The typical argument now supports a mix of functions for different variable types at which numeric or categorical covariates (non-focal terms) are held constant.

  • Clarification of how the re.form argument is set when using type = "random" resp. type = "fixed" in ggpredict().

  • hypothesis_test() now returns the standard error of contrasts or pairwise comparisons as attribute standard_error. This can be used to compute the test-statistic, if required. In forthcoming updates, there will be methods for insight::get_statistic() and parameters::model_parameters() to include standard errors and test-statistics in the output.

  • test_predictions() was added as an alias for hypothesis_test().

Bug fixes

  • Fixed issue in hypothesis_test() for mixed models, which sometimes failed when random effects group variables were numeric, and not factors.

Scientific Software - Peer-reviewed - R
Published by strengejacke over 2 years ago

ggeffects - ggeffects 1.3.1

New functions

  • johnson_neyman(), to create Johnson-Neyman intervals and plots from ggeffects objects.

Changes

  • Better automatic handling of offset-terms, both for predictions and generating plots with raw data. When the model formula contains an offset-term, and the offset term is fixed at a specific value, the response variable is now automatically transformed back to the original scale, and the offset-term is added to the predicted values. A warning is printed when model contains transformed offset-terms that are not fixed, e.g. via the condition argument.

  • ggeffect() now supports nestedLogit models.

Bug fixes

  • Fixed issue in hypothesis_test(), where the by argument did not work together with the collapse_levels argument.

  • Fixed issue in plot() method when adding raw data points for data frame that had now row names.

Scientific Software - Peer-reviewed - R
Published by strengejacke over 2 years ago

ggeffects - ggeffects 1.3.0

Breaking

  • To avoid confusion when adding raw data or residuals to plots, the jitter argument that is used to add some noice to data points to avoid overlapping now defaults to NULL. Formerly, a small jitter was added by default, leading to confusion when data points did not match the original data.

Changes

  • The plot() method gets a label.data argument, to add row names to data points when add.data = TRUE.

  • tibbles are always converted into data frames, to avoid issues.

  • hypothesis_test() gains a by argument, to specify a variable that is used to group the comparisons or contrasts. This is useful for models with interaction terms.

Bug fixes

  • Plotting residuals did not work when model object passed to ggpredict() were inside a list, or when called from inside functions (scoping issues).

  • Fixed issue where plotting raw data (i.e. plot(..., add.data = TRUE)) did not work when there were missing data in weight variables (i.e. when the regression model used weights).

  • Fixes issue in plot() when no term was specified in the call to ggpredict().

  • Fixed issues with robust estimation for models of package pscl.

  • Fixed issues introduced by breaking changes in marginaleffects.

Scientific Software - Peer-reviewed - R
Published by strengejacke almost 3 years ago

ggeffects - ggeffects 1.2.3

General

  • Support for nestedLogit (nestedLogit) models.

  • hyothesis_test() gains a scale argument, to explicitely modulate the scale of the contrasts or comparisons (e.g. "response" or "link", or "exp" to return transformed contrasts/comparisons).

  • hyothesis_test() now includes the response level for models with ordinal outcomes (and alike).

  • When ggpredict() is used inside functions and a name for a vector variable (passed as argument to that function) in terms is used, the variable is now correctly recognized.

  • Partial residuals (when plot(..., residuals = TRUE)) now supports more linear (mixed) models, including models from package lme (such as gls() or lme()).

  • For mixed models, type = "random" used to calculate prediction intervals that always accounted for random effects variances, leading to larger intervals. Using interval = "confidence" together with type = "random" now allows to calculate "usual" confidence intervals for random effects. This is usefule for predictions at specific group levels of random effects (when focal terms are only fixed effects, use type = "fixed" for regular confidence intervals).

  • The vcov.fun argument can now also be a function that returns a variance-covariance matrix.

  • The verbose argument in ggpredict() and hypothesis_test() now also toggle messages for the respective print() methods.

  • The print() method for hypothesis_test() has been revised and now provides more details for possible transformation of the scale of comparisons and contrasts.

  • The print() method now shows all rows by default when the focal term is a factor. If rows are not shown in the output, a message is printed to inform the user about truncated output.

  • A new vignette about using ggeffects in the context of an intersectional multilevel analysis of individual heterogeneity, using the MAIHDA framework.

Bug fixes

  • Fixed issue with wrong order of x-axis-labels for plots when the focal term on the x-axis was a character vector, where alphabetical order of values did not match order of predictions.

  • Fixed issues in hyothesis_test() for models with ordinal outcomes (and alike).

Scientific Software - Peer-reviewed - R
Published by strengejacke almost 3 years ago

ggeffects - ggeffects 1.2.2

General

  • Added a new [.ggeffects function, which allows to subset ggeffects objects in the same way as regular data frames, i.e. it is now possible to do: gge <- ggpredict(model, "x1") gge[c(1:2)]

  • Using a name for a vector variable in terms now works from inside functions. E.g., you can now do: foo <- function(data) { fit <- lm(barthtot ~ c12hour + c172code, data = data) v <- c(20, 50, 70) ggpredict(fit, terms = "c12hour [v]") } foo(efc)

  • The colors argument in plot() can now also be applied to single-colored plots.

  • hyothesis_test() gains a collapse_level argument, to collapse term labels that refer to the same levels into a singel unique level string.

Bug fixes

  • Fixed issue with misplaced residuals when x-axis was categorical and the factor levels were not in alphabetical order.

  • pool_predictions() now correctly handles models with transformed response variables (like log(y)) and returns the correct back-transformed pooled predictions (and their confidence intervals).

  • Fixed issue with wrong computation of confidence intervals for models of class clm from package ordinal.

  • Fixed failing tests due to changes in the logistf package, which now also supports emmeans. That means, ggemmeans() now also works for models from package logistf.

  • Fixed bug in plot() when partial residuals were added (i.e. residuals = TRUE) and collapse.group was provided (in case of mixed models).

  • Fixed issue with on-the-fly created factors inside formulas, which were not correctly treated as factors in the plot() method. This bug was related to recent changes in insight::get_data().

  • Fixed issue with wrong labels in hyothesis_test() for comparisons with many rows, when betas starting with same digit were specified (e.g. test = "(b1-b13)=(b3-b15)").

  • Fixed issue in hyothesis_test() for mixed models when focal terms included factors with factor levels that contained a comma.

  • Fixed issue with missing confidence intervals for mixed models when one of the variable names contains white space characters (e.g. y ~ 'x a' + xb).

Scientific Software - Peer-reviewed - R
Published by strengejacke about 3 years ago

ggeffects - ggeffects 1.2.0

Breaking

  • Confidence intervals of adjusted predictions now take the model's degrees of freedom into account, thereby leading to slightly larger intervals for models that do not have infinite degrees of freedom (like linear models with t-statistic).

New functions

  • hypothesis_test(), to compute contrasts and comparisons of predictions and test differences for statistical significance. Additionally, an accompanying vignette that explains the new function in detail is added.

  • install_latest(), to install the latest official package version from CRAN, or the latest development version from r-universe.

  • An as.data.frame() method was added, which converts ggeffects objects returned by ggpredict() into data frame, where standard column names are replaced by their related variable names.

General

  • Response values are now also back-transformed when these were transformed using log2(), log10() or log1p().

  • The terms argument can now also be a named list. Thus, instead of terms = c("score [30,50,70]", "status [low, middle]") one could also write terms = list(score = c(30,50,70), status = c("low", "middle")).

Scientific Software - Peer-reviewed - R
Published by strengejacke over 3 years ago

ggeffects - ggeffects 1.1.4

General

  • Reduced package dependencies. Packages sjlabelled and MASS were moved from imports to suggests. ggeffects is now a very lightweight package to compute adjusted predictions and estimated marginal means.

New supported models

  • logitr (package logitr)

Bug fixes

  • Fixed issue with wrong standard errors for predicting random effect groups for more multiple levels.

  • Fixed issue in ggemmeans(), which did not correctly averaged over character vectors when these were hold constant.

  • Fixed bug for models of class lme when type = "re" was requested.

Scientific Software - Peer-reviewed - R
Published by strengejacke over 3 years ago

ggeffects - 0.14.1.1

Scientific Software - Peer-reviewed - R
Published by strengejacke over 6 years ago

ggeffects - ggeffects 0.5.0

General

  • New vignette Different Output between Stata and ggeffects.

Changes to functions

  • ggpredict() now automatically back-transforms predictions to the response scale for model with log-transformed response.
  • ggeffect() and ggpredict() now automatically set numeric vectors with 10 or more unique values to representative values (see rprs_values()), if these are used as second or third value in the terms-argument (to represent a grouping structure).
  • Fix memory allocation issue in ggeffect().
  • rprs_values() is now exported.
  • The pretty-argument is deprecated, because prettifying values almost always makes sense - so this is done automatically.
  • ggpredict() now supports brmsfit-objects with categorical-family.
  • ggalleffect() has been removed. ggeffect() now plots effects for all model terms if terms = NULL.
  • gginteraction() and ggpoly() have been removed, as ggpredict() and ggeffect() are more efficient and generic for plotting interaction or polynomial terms.

Bug fixes

  • Fix issues with categorical or ordinal outcome models (polr, clm, multinom) for ggeffect().
  • Fix issues with confidence intervals for mixed models with log-transformed response value.
  • Fix issues with confidence intervals for generalized mixed models when response value was a rate or proportion created with cbind() in model formula.

Scientific Software - Peer-reviewed - R
Published by strengejacke almost 8 years ago

ggeffects - ggeffects 0.4.0

General

  • Removed alias names mem(), eff() and ame().
  • For mixed models (packages lme4, nlme, glmmTMB), the uncertainty of the random effect variances is now taken into account when type = "re".
  • Computing confidence intervals for mixed models should be much more memory efficient now, resulting less often in warnings about memory allocation problems.
  • Updated reference in CITATION to the publication in the Journal of Open Source Software.
  • A test-suite was added to the package.

New functions

  • pretty_range(), to create a pretty sequence of integers of a vector.

Changes to functions

  • ggpredict() gets a condition-argument to specify values at which covariates should be held constant, instead of their typical value.
  • The pretty-option for ggpredict() now calculates more values, leading to smoother plots.
  • The terms-argument in ggpredict() can now also select a range of feasible values for numeric values, e.g. terms = "age [pretty]". In contrast to the pretty-argument, which prettyfies all terms, you can selectively prettify specific terms with this option.
  • The terms-argument in ggpredict() now also supports all shortcuts that are possible for the mdrt.values-argument in gginteraction(), so for instance term = "age [meansd]" would return three values: mean(age) - sd(age), mean(age) and mean(age) + sd(age).
  • plot() gets some new arguments to control which plot-title to show or hide: show.title, show.x.title and show.y.title.
  • plot() gets a log.y argument to transform the y-axis to logarithmic scale, which might be useful for binomial models with predicted probabilities, or other models with log-alike link-functions.
  • The plot()-method for plotting all effects with ggpredict() (when term = NULL) now allows to arrange the plot in facets (using facets = TRUE).
  • Values in dot-argument for plot() are now passed down to ggplot::scale_y*(), to control the appearance of the y-axis (like breaks).

Bug fixes

  • Fixed issue with binomial models that used cbind(...) as response variable.
  • Fixed issue with suboptimal precision of confidence resp. prediction intervals for mixed models (packages lme4, nlme), which are now more accurate.

Scientific Software - Peer-reviewed - R
Published by strengejacke almost 8 years ago

ggeffects - ggeffects 0.3.4-1

Release with revisions suggested during JOSS-review.

Scientific Software - Peer-reviewed - R
Published by strengejacke almost 8 years ago

ggeffects - ggeffects 0.3.4

General

  • Prediction for glmmTMB-objects now compute proper confidence intervals, due to fix in package glmmTMB 0.2.1
  • If terms in ggpredict() is missing or NULL, marginal effects for each model term are calculated. ggpredict() then returns a list of data frames, which can also be plotted with plot().

Changes to functions

  • The jitter-argument from plot() now accepts a numeric value between 0 and 1, to control the width of the random variation in data points.
  • ggpredict() and ggeffect() can now predict transformed values, which is useful, for instance, to exponentiate predictions for log(term) on the original scale of the variable. See package vignette, section Marginal effects at specific values or levels for examples.

Bug fixes

  • Multivariate response models in brms with variable names with underscores and dots were not correctly plotted.

Scientific Software - Peer-reviewed - R
Published by strengejacke almost 8 years ago

ggeffects - ggeffects 0.3.3

General

  • Better support for multivariate-response-models from brms.
  • Support for cumulative-link-models from brms.
  • ggpredict() now supports linear multivariate response models, i.e. lm() with multiple outcomes.

Changes to functions

  • ggpredict() gets a pretty-argument to reduce and "prettify" the value range from variables in terms for predictions. This applies to all variables in terms with more than 25 unique values.

Bug fixes

  • Recognize negative binomial family from brmsfit-models.

Scientific Software - Peer-reviewed - R
Published by strengejacke about 8 years ago