NEWS.md
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"
).
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).
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()
.
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.
residuals.type
argument in plot()
is deprecated. Always using "working"
residuals.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.
interval
to ggemmeans()
, to either compute confidence or prediction intervals.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.residualize_over_grid()
is now exported.log1p()
and log(mu + x)
.type = "random"
or "zi_random"
), but random effects variances could not be calculated or were almost zero.multinom
models in ggemmeans()
.ggemmeans()
for models from nlme.plot()
for some models in ggeffect()
.terms = "predictor [exp]"
is no longer necessary.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.glm
since some time. Should be fixed now.ggpredict()
and rlmerMods
models when using factors as adjusted terms.ggeffect()
.ggpredict()
gets a new type
-option, "zi.prob"
, to predict the zero-inflation probability (for models from pscl, glmmTMB and GLMMadaptive).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.terms
-argument now also accepts the name of a variable to define specific values. See vignette Marginal Effects at Specific Values.type
-argument.1
.offset()
terms.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
.