NEWS.md
glmmTMB
-objects now compute proper confidence intervals, due to fix in package glmmTMB 0.2.1terms
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()
.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.ggpredict()
now supports linear multivariate response models, i.e. lm()
with multiple outcomes.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.ggpredict()
, ggeffect()
and gginteraction()
get a x.as.factor
-argument to preserve factor-class for the x
-column in the returned data frame.terms
-argument now also allows the specification of a range of numeric values in square brackets, e.g. terms = "age [30:50]"
.clm
-models don’t support full.data
-argument.emm()
did not work properly for some random effects models.brmsfit
-models from the brms-package.clm
-models from the ordinal-package.multinom
-models from the nnet-package.ppd
) now compute uncertainty intervals also for non-gaussian models.ggpredict()
now computes the weighted mean as typical value for predictors that are held constant.summary()
function, to provide information on predictions by grouping variables, and on constant values from adjustments.plot()
gets a show.legend
-argument to show or hide the legend of plots.plot()
gets a dot.alpha
-argument, to specify a different alpha-values for data points when plotting raw data.plot()
gets a jitter
-argument, to add a small amount of random variation to the location of data points when plotting raw data.plot()
and getter-functions (like get_title()
or get_x_labels()
) get a case
-argument, to convert labels into any case, using the snakecase-package.hurdle
, zeroinfl
, truncreg
and betareg
-models. Note, however, that due to some uncertainty, the intervals may not be “smooth”.ci.lvl
) were not always recognized.glmmTMB
-models.lme
-models.ggeffect()
, if the term in question was categorical.plot()
did not work for predictions at specific values (i.e. when certain levels of predictor where selected in square brackets).mermod
-objects did not work when model had only one fixed effects term.polr
models (pkg MASS).hurdle
and zeroinfl
models (pkg pscl).betareg
models (pkg betareg).truncreg
models (pkg truncreg).coxph
models (pkg survival).emm()
as convenient shortcut to compute the estimate marginal mean of the model’s response value.plot()
gets a use.theme
-argument, to use the default ggeffects-theme, or to use the default ggplot-theme.ggpredict()
computes proper confidence intervals for merMod- and lme-objects.plot()
-method, to better plot raw data.plot()
, argument rawdata
did not work for models with discrete binary response.lme
and glmmTMB
.