NOTE: for more news about the package, see https://github.com/florianhartig/DHARMa/releases
For hierarchical models (GLMMs), we changed the default
simulations from the supported model’s default (mostly unconditional) to
conditional simulations (for most of the supported models and packages).
This represents a major change in the calculated scaled residuals and is
implemented to ensure higher power in dispersion and other tests. We
expect different results for calculated residuals using this or older
DHARMa versions. For compatibility, you can use the
argument simulateREs = "user-specified" to change back to
the model’s default (older DHARMa versions). See package
vignette for more details.
In plotResiduals, we increased the threshold for the
automatic change from quantile regression lines to the spline: from
2,000 to 10,000 data points. When it happens, a message is displayed to
warn users. Also, when quantreg = F, the color of the
spline was changed to black because there is no test associated with the
line (as there is for quantile regression).
In plotResiduals, the argument form has
a new functionality. Beyond the syntax data$predictor, it
allows now to use the formula structure ~predictor,
~predictor1+predictor2, ~.,
~predictor|group and
~predictor|group == "group_level" to plot the residuals
against specific/all predictors and grouping variables/levels. This
works for most of the supported model functions. It solves problems with
NAs in datasets that were excluded by the model. #407 / #425
In testCategorical,
recalculateResiduals, testQuantiles,
testSpatialAutocorrelation and
testTemporalAutocorrelation, additional predictors
(catPred, group, sel, predictor,
time, x,y- respectively) can now be specified
as a formula (similar to plotResiduals). This handles rows
with NAs that were excluded by the model automatically.
brms is now supported by DHARMa for simple models,
i.e. models that could also be fit using glmmTMB (no multi-response,
multinomial or structural equation models).
Fixing error in runBenchmarks.
Fixing inconsistency in testQuantiles() #465. Including rankTransform() help function.
Using 95% confidence intervals for confidence bands in plots for testQuantiles(). Before, we were using standard errors (~ 68% CI).
Vectorize randomization of residuals to improve speed - PR#493 contributed by StaffanBetner.
Adding color legend to testSpatialAutocorrelation().
New function plotResidualsAll() to plot residuals against multiple predictors/all predictors of the model via plotResiduals().
New function getPredictorNames() extracts names of fixed and random effects (predictors) from a model.
testCategorical() now allows additional arguments to boxplot via …, mainly to allow for appropriate x-axis labels when running plotResidualsAll().
Improved appearance of user-specified titles via “main” in plotResiduals().
included option to simulate mgcv models using the functions implemented in mgcViz which should improve mgcv compatibility with DHARMa
Added option to include plot title in plot() #320
This is actually a bugfix release for 0.3.4, but on reflection I decided that 0.4.0 should have been a minor release, so I pushed the version number up to 0.4.0
0.3.4 is a relatively important release with various minor improvements a smaller new features, most noteworthy the support of glmmAdaptive