mo()with numeric predictors, which only allow to predict for values that are actually present in the data.
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
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.
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).
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).
Fixed issue in
print() for survival-models.
Fixed issue with
type = "simulate" for
Fixed issue with
gamlss models that had
random() function in the model formula.
Fixed issue with incorrect back-transformation of predictions for
plot()is deprecated. Always using
ggemmeans(), to either compute confidence or prediction intervals.
pool_predictions(), to pool multiple
ggeffectsobjects. This can be used when predicted values or estimated marginal means are calculated for models fit to multiple imputed datasets.
residualize_over_grid()is now exported.
log(mu + x).
type = "random"or
"zi_random"), but random effects variances could not be calculated or were almost zero.
ggemmeans()for models from nlme.
plot()for some models in
terms = "predictor [exp]"is no longer necessary.
plot()now can also create partial residuals plots. There, arguments
residuals.linewere added to add partial residuals, the type of residuals and a possible loess-fit regression line for the residual data.
glmsince some time. Should be fixed now.
rlmerModsmodels when using factors as adjusted terms.
ggpredict()gets a new
"zi.prob", to predict the zero-inflation probability (for models from pscl, glmmTMB and GLMMadaptive).
add.data = TRUEin
plot(), the raw data points are also transformed accordingly.
add.data = TRUEfirst adds the layer with raw data, then the points / lines for the marginal effects, so raw data points to not overlay the predicted values.
terms-argument now also accepts the name of a variable to define specific values. See vignette Marginal Effects at Specific Values.
plot(rawdata = TRUE)now also works for objects from
ggpredict()now computes confidence intervals for predictions from
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.
clmm-models, when group factor in random effects was numeric.
brmultinom(package brglm2) and models from packages bamlss and R2BayesX.
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.
vcov()function to calculate variance-covariance matrix for marginal effects.
ggeffect(), when one term was a character vector.
ggpredict()now supports cumulative link and ordinal vglm models from package VGAM.
termsincluded random effects.
add.datais an alias for the
ggemmeans()now also support predictions for gam models from
values_at()is an alias for
ggpredict()now supports prediction intervals for models from MCMCglmm.
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).
ggemmeans()now can calculate marginal effects for specific values from up to three terms (i.e.
termscan be of lenght four now).
plot()now also applies to error bars for categorical variables on the x-axis.
terms = "predictor [1:10]") can now be changed with
terms = "predictor [1:10 by=.5]"(see also vignette Marginal Effects at Specific Values).
ggpredict()) now also works for following model-objects:
polr(and probably also
gls, not tested yet).
interval-argument, to compute prediction intervals instead of confidence intervals.
plot.ggeffects()now allows different horizontal and vertical jittering for
jitteris a numeric vector of length two.
AsIs-conversion from division of two variables as dependent variable, e.g.
I(amount/frequency), now should work.