plot(rawdata = TRUE)
now also works for objects from ggemmeans()
.ggpredict()
now computes confidence intervals for predictions from geeglm
models.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.print()
.clmm
-models, when group factor in random effects was numeric.bracl
, 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.show_pals()
).vcov()
function to calculate variance-covariance matrix for marginal effects.ggemmeans()
now also accepts type = "re"
and type = "re.zi"
, to add random effects variances to prediction intervals for mixed models....
is now passed down to the predict()
-method for gamlss-objects, so predictions can be computed for sigma, nu and tau as well.ggeffect()
, when one term was a character vector.ggaverage()
is discouraged, and so it was removed.rprs_values()
is now deprecated, the function is named values_at()
, and its alias is representative_values()
.x.as.factor
-argument defaults to TRUE
.ggpredict()
now supports cumulative link and ordinal vglm models from package VGAM.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.values_at()
is an alias for rprs_values()
.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).ci.style
-argument from plot()
now also applies to error bars for categorical variables on the x-axis.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)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).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.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
.ggemmeans()
now supports type = "fe.zi"
for glmmTMB-models, i.e. predicted values are conditioned on the fixed effects and the zero-inflation components of glmmTMB-models.ggpredict()
now supports MCMCglmm, ivreg and MixMod (package GLMMadaptive) models.ggemmeans()
now supports MCMCglmm and MixMod (package GLMMadaptive) models.ggpredict()
now computes confidence intervals for gam models (package gam).new_data()
, to create a data frame from all combinations of predictor values. This data frame typically can be used for the newdata
-argument in predict()
, in case it is necessary to quickly create an own data frame for this argument.ggpredict()
no longer stops when predicted values with confidence intervals for glmmTMB- and other zero-inflated models can’t be computed with type = "fe.zi"
, and only returns the predicted values without confidence intervals.ggpredict()
fails to compute confidence intervals, a more informative error message is given.plot()
gets a connect.lines
-argument, to connect dots from plots with discrete x-axis.ggpredict()
did not work with glmmTMB- and other zero-inflated models, when type = "fe.zi"
and model- or zero-inflation formula had a polynomial term that was held constant (i.e. not part of the terms
-argument).type = "fe.zi"
could not be computed when the model contained polynomial terms and a very long formula (issue with deparse()
, cutting off very long formulas).plot()
-method put different spacing between groups when a numeric factor was used along the x-axis, where the factor levels where non equal-spaced.lm
in ggeffects()
.type = "fe"
and type = "re"
return population-level predictions for mixed effects models (lme4, glmmTMB). The difference is that type = "re"
also takes the random effect variances for prediction intervals into account. Predicted values at specific levels of random effect terms is described in the package-vignettes Marginal Effects for Random Effects Models and Marginal Effects at Specific Values.terms
-argument.plot()
. Use show_pals()
to show all available palettes.ggpredict()
and ggeffect()
now support brms-models with additional response information (like trial()
).ggpredict()
now supports Gam, glmmPQL, clmm, and zerotrunc-models.ggemmeans()
-function. Since this function is quite new, there still might be some bugs, though.ggemmeans()
to compute marginal effects by calling emmeans::emmeans()
.theme_ggeffects()
, which can be used with ggplot2::theme_set()
to set the ggeffects-theme as default plotting theme. This makes it easier to add further theme-modifications like sjPlot::legend_style()
or sjPlot::font_size()
.type = "sim"
) to ggpredict()
, currently for models of class glmmTMB and merMod.x.cat
is a new alias for the argument x.as.factor
.plot()
-method gets a ci.style
-argument, to define different styles for the confidence bands for numeric x-axis-terms.print()
-method gets a x.lab
-argument to print value labels instead of numeric values if x
is categorical.emm()
now also supports all prediction-types, like ggpredict()
.ggeffect()
, which did not work if data had variables with more that 8 digits (fractional part longer than 8 numbers).ppd = TRUE
.type = "fe.zi"
, which could mess up the correct order of predicted values for x
.type = "fe.zi"
or type = "re.zi"
, when first terms had the [all]
-tag.print()
-method for mixed effects models, when predictions were conditioned on all model terms and adjustment was only done for random effects (output-line “adjusted for”).terms
included a factor and contrasts
were set to other values than contr.treatment
.glm
-object and heteroskedasticity-consistent covariance matrix estimation.condition
-argument was not always considered for some model types when calculating confidence intervals for predicted values.