Changelog • 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().

Bug fixes

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

ggeffects 1.3.1

CRAN release: 2023-09-05

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.

ggeffects 1.3.0

CRAN release: 2023-08-21

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.

ggeffects 1.2.3

CRAN release: 2023-06-11

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).

ggeffects 1.2.2

CRAN release: 2023-05-04

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).

ggeffects 1.2.1

CRAN release: 2023-04-02

General

Support for mblogit (mclogit), phylolm and phyloglm (phylolm) models.

Changes to functions

hypothesis_test() gains an equivalence argument, to compute tests of practical equivalence for contrasts and comparisons.
The message whether contrasts or comparisons from hypothesis_test() are on the link-scale is now printed below the table.
Dot arguments (...) in hypothesis_test() are now passed to the functions in marginaleffects, thereby allowing to use further options in functions marginaleffects::predictions(), like transform etc.

Bug fixes

Fixed issues in hypothesis_test() for mixed models with one focal term only, and when this term was categorical.

ggeffects 1.2.0

CRAN release: 2023-02-24

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")).

ggeffects 1.1.5

CRAN release: 2023-01-25

General

Minor changes to meet forthcoming update of insight.
ggpredict() or ggemmeans() get a verbose argument to suppress some messages and warnings when calling

ggeffects 1.1.4

CRAN release: 2022-10-23

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.

ggeffects 1.1.3

CRAN release: 2022-08-07

Bug fixes

Fix wrong computations of predictions for arm::bayesglm() models.
Fix CRAN check issues.

ggeffects 1.1.2

CRAN release: 2022-04-10

General

Speed improvement for some models when calculating uncertainty intervals of predictions.
Minor fixes.

ggeffects 1.1.1

CRAN release: 2021-07-29

Changes to functions

Add more informative error message for brmsfit models using mo() with numeric predictors, which only allow to predict for values that are actually present in the data.

Bug fixes

Fixed issue with adding raw data points for plots from logistic regression models, when the response variable was no factor with numeric levels.
Fixed issues with CRAN checks.

ggeffects 1.1.0

CRAN release: 2021-04-30

New supported models

orm (package rms)

Breaking Changes

Prediction intervals (where possible, or when type = "random"), are now always based on sigma^2 (i.e. insight::get_sigma(model)^2). This is in line with interval = "prediction" for lm, or for predictions based on simulations (when type = "simulate").
print() now uses the name of the focal variable as column name (instead) of "x").

New function

collapse_by_group(), to generate a data frame where the response value of the raw data is averaged over the levels of a (random effect) grouping factor.

General

A new vignette was added related to the definition and meaning of “marginal effects” and “adjusted predictions”. To be more strict and to avoid confusion with the term “marginal effect”, which meaning may vary across fields, either “marginal effects” was replaced by “adjusted predictions”, or “adjusted predictions” was added as term throughout the package’s documentation and vignettes.
Allow confidence intervals when predictions are conditioned on random effect groups (i.e. when type = "random" and terms includes a random effect group factor).
Predicted response values based on simulate() (i.e. when type = "simulate") is now possible for more model classes (see ?ggpredict).
ggpredict() now computes confidence intervals for some edge cases where it previously failed (e.g. some models that do not compute standard errors for predictions, and where a factor was included in the model and not the focal term).
plot() gains a collapse.group argument, which - in conjunction with add.data - averages (“collapses”) the raw data by the levels of the group factors (random effects).
data_grid() was added as more common alias for new_data().

Bug fixes

ggpredict() and plot() for survival-models now always start with time = 1.
Fixed issue in print() for survival-models.
Fixed issue with type = "simulate" for glmmTMB models.
Fixed issue with gamlss models that had random() function in the model formula.
Fixed issue with incorrect back-transformation of predictions for geeglm models.

ggeffects 1.0.2

CRAN release: 2021-03-17

Breaking changes

residuals.type argument in plot() is deprecated. Always using "working" residuals.

General

pretty_range() and values_at() can now also be used as function factories.
plot() gains a limit.range argument, to limit the range of the prediction bands to the range of the data.

Bug fixes

Fixed issue with unnecessary back-transformation of log-transformed offset-terms from glmmTMB models.
Fixed issues with plotting raw data when predictor on x-axis was a character vector.
Fixed issues from CRAN checks.

ggeffects 1.0.1

CRAN release: 2020-12-14

General

Fixed CRAN check issues.
Added argument interval to ggemmeans(), to either compute confidence or prediction intervals.

ggeffects 1.0.0

CRAN release: 2020-11-29

New supported models

averaging (package MuMIn)

New functions

pool_predictions(), to pool multiple ggeffects objects. This can be used when predicted values or estimated marginal means are calculated for models fit to multiple imputed datasets.

General

The function residualize_over_grid() is now exported.
The back-transformation of the response-variable (if these were log- or square root-transformed in the model) now also works with square root-transformations and correctly handles log1p() and log(mu + x).
Since standard errors were on the link-scale and not back-transformed for non-Gaussian models, these are now no longer printed (to avoid confusion between standard errors on the link-scale and predictions and confidence intervals on the response-scale).

Bug fixes

Fixed issue for mixed models when predictions should be conditioned on random effects variances (e.g. type = "random" or "zi_random"), but random effects variances could not be calculated or were almost zero.
Fixed issue with confidence intervals for multinom models in ggemmeans().
Fixed issue in ggemmeans() for models from nlme.
Fixed issue with plot() for some models in ggeffect().
Fixed issue with computation of confidence intervals for zero-inflated models with offset-term.

ggeffects 0.16.0

CRAN release: 2020-09-13

Breaking changes

Package insight since version 0.9.5 now returns the “raw” (untransformed, i.e. original) data that was used to fit the model also for log-transformed variables. Thus, exponentiation like using terms = "predictor [exp]" is no longer necessary.

New supported models

mlogit (package mlogit)

General

plot() now can also create partial residuals plots. There, arguments residuals, residuals.type and residuals.line were added to add partial residuals, the type of residuals and a possible loess-fit regression line for the residual data.

Bug fixes

The message for models with a back-transformation to the response scale (all non-Gaussian models), that standard errors are still on the link-scale, did not show up for models of class glm since some time. Should be fixed now.
Fixed issue with ggpredict() and rlmerMods models when using factors as adjusted terms.
Fixed issue with brms-multi-response models.

ggeffects 0.15.1

CRAN release: 2020-07-27

New supported models

mclogit (package mclogit)

Bug fixes

Fixed issues due to latest rstanarm update.
Fixed some issues around categorical/cumulative brms models when the outcome is numeric.
Fixed bug with factor level ordering when plotting raw data from ggeffect().

ggeffects 0.15.0

CRAN release: 2020-06-16

Changes to functions

ggpredict() gets a new type-option, "zi.prob", to predict the zero-inflation probability (for models from pscl, glmmTMB and GLMMadaptive).
When model has log-transformed response variable and add.data = TRUE in plot(), the raw data points are also transformed accordingly.
plot() with add.data = TRUE first adds the layer with raw data, then the points / lines for the marginal effects, so raw data points to not overlay the predicted values.
The terms-argument now also accepts the name of a variable to define specific values. See vignette Marginal Effects at Specific Values.

Bug fixes

Fix issues in cluster-robust variance-covariance estimation when vcov.type was not specified.

ggeffects 0.14.3

CRAN release: 2020-04-20

General

Fixed issues to due changes in other CRAN packages.

ggeffects 0.14.2

CRAN release: 2020-03-14

General

ggeffects now requires glmmTMB version 1.0.0 or higher.
Added human-readable alias-options to the type-argument.

Bug fixes

Fixed issue when log-transformed predictors where held constant and their typical value was negative.
Fixed issue when plotting raw data to a plot with categorical predictor in the x-axis, which had numeric factor levels that did not start at 1.
Fixed issues for model objects that used (log) transformed offset() terms.

ggeffects 0.14.1

CRAN release: 2020-01-28

General

Reduce package dependencies.
New package-vignette (Cluster) Robust Standard Errors.

New supported models

mixor (package mixor), cgam, cgamm (package cgam)

Bug fixes

Fix CRAN check issues due to latest emmeans update.

ggeffects 0.14.0

CRAN release: 2019-12-16

Breaking Changes

The argument x.as.factor is considered as less useful and was removed.

New supported models

fixest (package fixest), glmx (package glmx).

General

Reduce package dependencies.
plot(rawdata = TRUE) now also works for objects from ggemmeans().
ggpredict() now computes confidence intervals for predictions from geeglm models.
For brmsfit models with trials() as response variable, ggpredict() used to choose the median value of trials were the response was hold constant. Now, you can use the condition-argument to hold the number of trials constant at different values.
Improve print().

Bug fixes

Fixed issue with clmm-models, when group factor in random effects was numeric.
Raw data is no longer omitted in plots when grouping variable is continuous and added raw data doesn’t numerically match the grouping levels (e.g., mean +/- one standard deviation).
Fix CRAN check issues due to latest geepack update.

ggeffects 0.13.0

CRAN release: 2019-11-08

Breaking Changes

The use of emm() is discouraged, and so it was removed.

New supported models

bracl, brmultinom (package brglm2) and models from packages bamlss and R2BayesX.

General

Updated package dependencies.
plot() now uses dodge-position for raw data for categorical x-axis, to align raw data points with points and error bars geoms from predictions.
Updated and re-arranged internal color palette, especially to have a better behaviour when selecting colors from continuous palettes (see show_pals()).

New functions

Added a vcov() function to calculate variance-covariance matrix for marginal effects.

Changes to Functions

ggemmeans() now also accepts type = "re" and type = "re.zi", to add random effects variances to prediction intervals for mixed models.
The ellipses-argument ... is now passed down to the predict()-method for gamlss-objects, so predictions can be computed for sigma, nu and tau as well.

Bug fixes

Fixed issue with wrong order of plot x-axis for ggeffect(), when one term was a character vector.

ggeffects 0.12.0

CRAN release: 2019-09-03

Breaking Changes

The use of ggaverage() is discouraged, and so it was removed.
The name rprs_values() is now deprecated, the function is named values_at(), and its alias is representative_values().
The x.as.factor-argument defaults to TRUE.

General

ggpredict() now supports cumulative link and ordinal vglm models from package VGAM.
More informative error message for clmm-models when terms included random effects.
add.data is an alias for the rawdata-argument in plot().
ggpredict() and ggemmeans() now also support predictions for gam models from ziplss family.

Changes to Functions

Improved print()-method for ordinal or cumulative link models.
The plot()-method no longer changes the order of factor levels for groups and facets.
pretty_data() gets a length() argument to define the length of intervals to be returned.

Bug fixes

Added “population level” to output from print-method for lme objects.
Fixed issue with correct identification of gamm/gamm4 models.
Fixed issue with weighted regression models from brms.
Fixed broken tests due to changes of forthcoming effects update.

ggeffects 0.11.0

CRAN release: 2019-07-01

General

Revised docs and vignettes - the use of the term average marginal effects was replaced by a less misleading wording, since the functions of ggeffects calculate marginal effects at the mean or at representative values, but not average marginal effects.
Replace references to internal vignettes in docstrings to website-vignettes, so links on website are no longer broken.
values_at() is an alias for rprs_values().

New supported models

betabin, negbin (package aod), wbm (package panelr)

Changes to functions

ggpredict() now supports prediction intervals for models from MCMCglmm.
ggpredict() gets a back.transform-argument, to tranform predicted values from log-transformed responses back to their original scale (the default behaviour), or to allow predictions to remain on log-scale (new).
ggpredict() and ggemmeans() now can calculate marginal effects for specific values from up to three terms (i.e. terms can be of lenght four now).
The ci.style-argument from plot() now also applies to error bars for categorical variables on the x-axis.

Bug fixes

Fixed issue with glmmTMB models that included model weights.

ggeffects 0.10.0

CRAN release: 2019-05-13

General

Better support, including confidence intervals, for some of the already supported model types.
New package-vignette Logistic Mixed Effects Model with Interaction Term.

New supported models

gamlss, geeglm (package geepack), lmrob and glmrob (package robustbase), ols (package rms), rlmer (package robustlmm), rq and rqss (package quantreg), tobit (package AER), survreg (package survival)

Changes to functions

The steps for specifying a range of values (e.g. terms = "predictor [1:10]") can now be changed with by, e.g. terms = "predictor [1:10 by=.5]" (see also vignette Marginal Effects at Specific Values).
Robust standard errors for predictions (see argument vcov.fun in ggpredict()) now also works for following model-objects: coxph, plm, polr (and probably also lme and gls, not tested yet).
ggpredict() gets an interval-argument, to compute prediction intervals instead of confidence intervals.
plot.ggeffects() now allows different horizontal and vertical jittering for rawdata when jitter is a numeric vector of length two.

Bug fixes

Models with AsIs-conversion from division of two variables as dependent variable, e.g. I(amount/frequency), now should work.
ggpredict() failed for MixMod-objects when ci.lvl=NA.