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Fixed issues with renamed arguments in upstream package ggeffects.
Several minor bug fixes.
tab_model(), plot_model() and
plot_models() get a std.response argument, to
include or exclude the response variable from standardization.Fixed test issues when no internet connection avaiable.
Minor fixes and improvements.
knit_print() generics.tab_model() now works properly with forthcoming
parameters update.plot_models() did not work properly for Bayesian
models.tab_model() also gains an encoding
argument.tab_df() and tab_dfs() no longer set the
argument show.rownames to TRUE. Therefore,
both functions now use row numbers as row names, if no other rownames
are present.tab_dfs() also gains a digits
argument.df.method in tab_model() did not
accept all available options that were documented.dv.labels = "" in tab_model(), the
row with names of dependent variables is omitted.minus.sign argument in tab_model() now
works.show.std = TRUE in tab_model() did not
exponentiate standardized coefficients for non-Gaussian models.tab_model() gains an argument df.method,
which will replace the less generic p.val argument in the
future. Currently, df.method is an alias of
p.val.plot_stackfrq() when
weights were applied.plot_stackfrq() when weights were applied
and items should be sorted.plot_models() for models without
intercept.plot_models()
when showing p-stars.plot_model() with
type = "int" in detecting interaction terms when these were
partly in parenthesis (like a * (b + c)).tab_model() with arguments
show.stat = TRUE and show.std = TRUE, where
the related statistic and CI columns for standardized coefficients were
not shown.tab_model() for brmsfit models
that did no longer show random effects information after the last update
from the performance package.show.rownames in
tab_df().vcov.fun in
tab_model() or plot_model()) now also uses and
thus accepts estimation-types from package clubSandwich.tab_model() now accepts all options for
p.val that are supported by
parameters::model_parameters().p.style argument in tab_model() was
slightly revised, and now also accepts "scientific" as
option for scientific notation of p-values.tab_model() gets a digits.re argument to
define decimal part of the random effects summary.plot_models() gains value.size and
line.size arguments, similar to
plot_model().plot_models() should sort coefficients in their natural
order now.plot_xtab() with wrong order of legend
labels.plot_models() with wrong axis title for
exponentiated coefficients.tab_model() that did not show standard
error of standardized coefficients when
show.se = TRUE.tab_model() and plot_model() now support
clogit models (requires latest update of package
insight).tab_model() gets a p.adjust argument to
adjust p-values for multiple comparisons.tab_model(), plot_model() and
plot_models() get a robust-argument to easily
compute standard errors, confidence intervals and p-values based on
robust estimation of the variance-covariance matrix. robust
is just a convenient shortcut for vcov.fun and
vcov.type.tab_model() and
plot_model() for certain cases when coefficients could not
be estimated and were NA.tab_model() with
collapse.ci for Bayesian models.tab_model() when p.val="kr"
and show.df=TRUE.tab_model() with formatting issues of
p-values when standardized coefficients where requested.tab_model() due to changes in other
packages sjPlot depends on.sjt.itemanalysis() is now named
tab_itemscale().sjt.xtab() is now named tab_xtab().tab_model() of robust estimation
in general and Kenward-Roger or Satterthwaite approximations in
particular for linear mixed models.tab_df() now uses value labels for factors
instead of numeric values.tab_model() gets arguments bootstrap,
iterations and seed to return bootstrapped
estimates.tab_model() with detecting labels when
auto.label = TRUE.tab_model() for negative binomial hurdle
mixed models (i.e. glmmTMB models with truncated
negative-binomial family).tab_model() with
show.reflvl = TRUE.tab_model() where labels for coefficients
where not matching the correct coefficients.plot_model() or
tab_model()) now uses standardization based on refitting
the model.plot_model() gets type = "emm" as marginal
effects plot type, which is similar to type = "eff". See Plotting
Marginal Effects of Regression Models for details.verbose-argument in view_df() now
defaults to FALSE.show_pals()).sort.est = NULL in plot_model() now
preserves original order of coefficients.plot_frq()
for non-labelled, numeric values.plot_frq() when plotting factors.string.std_ci and string.std_se
are no longer ignored in tab_model().performance::principal_component() by
parameters::principal_component().sjp.grpfrq() is now names
plot_grpfrq().sjp.xtab() is now names plot_xtab().plot_grid() gets a tags-argument to add
tags to plot-panels.plot_stackfrq() for data frames with many
missing values.plot_frq() when
vector had more labels than values.tab_model() where
show.reflvl = TRUE did not insert the reference category in
first place, but in alphabetical order.show_sjplot_pals()).tab_model() now supports gamlss models.tab_df() gets a digits argument, to round
numeric values in output.tab_model() with
show.df = TRUE for lmerModLmerTest.tab_stackfrq() when items had different
amount of valid values.sjp.stackfrq() was renamed to
plot_stackfrq().sjt.stackfrq() was renamed to
tab_stackfrq().plot_likert()group.legend.options. The
ordering now defaults to row wise and the user can force all categories
onto a single row.tab_model()wbm()-models from the
panelr-package.show.aicc-argument to show the second order
AIC.show.reflvl-argument to show the reference level
of factors.string.std_se and
string.std_ci-argument to change the column header for
standard errors and confidence intervals of standardized
coefficients.show.ci50 defaults to FALSE now.sjt.itemanalysis()sjt.itemanalysis() now works on ordered factors. A
clearer error message was added when unordered factors are used. The old
error message was not helpful.factor.groups argument can now be
"auto" to detect factor groups based on a pca with Varimax
rotation.sjp.stackrq()sjp.stackfrq() was renamed to
plot_stackfrq().sjp.stackfrq() (now named:
plot_stackfrq()) gets a show.n-argument to
also show count values. This option can be combined with
show.prc.sjp.stackfrq() (now named:
plot_stackfrq()) now also works on grouped data
frames.plot_model() now supports wbm()-models
from the panelr-package.plot_model(type = "int") now also recognized
interaction terms with : in formula.string.est in tab_model() did not
overwrite the default label for the estimate-column-header.tab_model() for mixed models that can’t
compute R2.tab_model() when printing robust standard
errors and CI (i.e. when using arguments vcov*).plot_likert() option
reverse.scale = TRUE resulted in
values = "sum.inside" being outside and the other way
around. This is fixed now.view_df() mixed up labels and frequency values when
value labels were present, but no such values were in the data.wrap.labels in plot_frq() did not
properly work for factor levels.plot_models() that stopped for some
models.sjt.stackfrq(), when
show.na = TRUE and some items had zero-values.dplyr::n(), to meet changes in dplyr 0.8.0.plot_model() and tab_model() now support
MixMod-objects from package GLMMadpative,
mlogit- and gmnl-models.sjp.kfold_cv() was renamed to
plot_kfold_cv().sjp.frq() was renamed to plot_frq().tab_model() gets a show.ngrps-argument,
which adds back the functionality to print the number of random effects
groups for mixed models.tab_model() gets a show.loglik-argument,
which adds back the functionality to print the model’s
log-Likelihood.tab_model() gets a strings-argument, as
convenient shortcut for setting column-header strings.tab_model() gets additional arguments
vcov.fun, vcov.type and vcov.args
that are passed down to sjstats::robust(), to calculate
different types of (clustered) robust standard errors.p.style-argument now also allows printing both
numeric p-values and asterisks, by using
p.style = "both".plot_likert() gets a reverse.scale
argument to reverse the order of categories, so positive and negative
values switch position.plot_likert() gets a groups argument, to
group items in the plot (thanks to @ndevln).grid.range in plot_likert() now
may also be a vector of length 2, to define diffent length for the left
and right x-axis scales.plot_frq() (former sjp.frq()) now has
pipe-consistent syntax, enables plotting multiple variables in one
function call and supports grouped data frames.plot_model() gets additional arguments
vcov.fun, vcov.type and vcov.args
that are passed down to sjstats::robust(), to calculate
different types of (clustered) robust standard errors.sjt.xtab(), sjp.xtab(),
plot_frq() and sjp.grpfrq() get a
drop.empty()-argument, to drop values / factor levels with
no observations from output.plot_model(..., type = "diag").color ="bw" and legend.title was
specified.view_df() did not truncate frequency- and
percentage-values for variables where value labels were truncated to a
certain maximum number.tab_model() did not print number of observations for
coxph-models.Following functions are now defunct:
sjt.lm(), sjt.glm(),
sjt.lmer() and sjt.glmer(). Please use
tab_model() instead.tab_model() supports printing simplex parameters of
monotonic effects of brms models.tab_model() gets a prefix.labels-argument
to add a prefix to the labels of categorical terms.rotation-argument in sjt.pca() and
sjp.pca() now supports all rotations from
psych::principal().plot_model() no longer automatically changes the
plot-type to "slope" for models with only one predictor
that is categorical and has more than two levels.type = "eff" and type = "pred" in
plot_model() did not work when terms was not
specified.tab_model(),
the confidence intervals and p-values are now re-calculated and adjusted
based on the robust standard errors.colors = "bw" was not recognized correctly for
plot_model(..., type = "int").sjp.frq() with correct axis labels for
non-labelled character vectors.sjt.lm(), sjt.glm(),
sjt.lmer() and sjt.glmer() are now deprecated.
Please use tab_model() instead.dot.size and line.size in
plot_model() now also apply to marginal effects and
diagnostic plots.plot_model() now uses a free x-axis scale in facets for
models with zero-inflated part.plot_model() now shows multiple plots for models with
zero-inflated parts when grids = FALSE.tab_model() gets a p.style and
p.threshold argument to indicate significance levels as
asteriks, and to determine the threshold for which an estimate is
considered as significant.plot_model() and plot_models() get a
p.threshold argument to determine the threshold for which
an estimate is considered as significant.plot_likert().tab_model() now also accepts multiple model-objects
stored in a list as argument, as stated in the
help-file.file-argument now works again in
sjt.itemanalysis().show.ci in tab_model() did not
compute confidence intervals for different levels.sjp.scatter() was revised and renamed to
plot_scatter(). plot_scatter() is
pipe-friendly, and also works on grouped data frames.sjp.gpt() was revised and renamed to
plot_gpt(). plot_gpt() is pipe-friendly, and
also works on grouped data frames.sjp.scatter() was renamed to
plot_scatter().sjp.likert() was renamed to
plot_likert().sjp.gpt() was renamed to plot_gpt().sjp.resid() was renamed to
plot_residuals().brmsfit-objects with
categorical-family for plot_model() and
tab_model().tab_model() gets a show.adj.icc-argument,
to also show the adjusted ICC for mixed models.tab_model() gets a col.order-argument,
reorder the table columns.hide.progress in view_df() is
deprecated. Please use verbose now.statistics-argument in sjt.xtab() gets
a "fisher"-option, to force Fisher’s Exact Test to be
used.Following functions are now defunct:
sjp.lm(), sjp.glm(),
sjp.lmer(), sjp.glmer() and
sjp.int(). Please use plot_model()
instead.sjt.frq(). Please use
sjmisc::frq(out = "v") instead.lmerModLmerTest
objects.show.std) in tab_model().tab_model() as replacement for sjt.lm(),
sjt.glm(), sjt.lmer() and
sjt.glmer(). Furthermore, tab_model() is
designed to work with the same model-objects as
plot_model().scale_fill_sjplot() and scale_color_sjplot().
These provide predifined colour palettes from this package.show_sjplot_pals() to show all predefined colour
palettes provided by this package.sjplot_pal() to return colour values of a specific
palette.Following functions are now deprecated:
sjp.lm(), sjp.glm(),
sjp.lmer(), sjp.glmer() and
sjp.int(). Please use plot_model()
instead.sjt.frq(). Please use
sjmisc::frq(out = "v") instead.Following functions are now defunct:
sjt.grpmean(), sjt.mwu() and
sjt.df(). The replacements are
sjstats::grpmean(), sjstats::mwu() and
tab_df() resp. tab_dfs().plot_model() and plot_models() get a
prefix.labels-argument, to prefix automatically retrieved
term labels with either the related variable name or label.plot_model() gets a show.zeroinf-argument
to show or hide the zero-inflation-part of models in the plot.plot_model() gets a jitter-argument to add
some random variation to data points for those plot types that accept
show.data = TRUE.plot_model() gets a legend.title-argument
to define the legend title for plots that display a legend.plot_model() now passes more arguments in
... down to ggeffects::plot() for marginal
effects plots.plot_model() now plots the zero-inflated part of the
model for brmsfit-objects.plot_model() now plots multivariate response models,
i.e. models with multiple outcomes.plot_model()
(type = "diag") can now also be used with
brmsfit-objects.plot_model()
(type = "diag") for Stan-models (brmsfit or
stanreg resp. stanfit) can now be set with the
axis.lim-argument.grid.breaks-argument for plot_model()
and plot_models() now also takes a vector of values to
directly define the grid breaks for the plot.plot_model() and plot_models() when the
grid.breaks-argument is of length one.terms-argument for plot_model() now
also allows the specification of a range of numeric values in square
brackets for marginal effects plots,
e.g. terms = "age [30:50]" or
terms = "age [pretty]".terms- and
rm.terms-arguments for plot_model() now also
allows specification of factor levels for categorical terms.
Coefficients for the indicted factor levels are kept resp. removed (see
?plot_model for details).plot_model() now supports clmm-objects
(package ordinal).plot_model(type = "diag") now also shows random-effects
QQ-plots for glmmTMB-models, and also plots random-effects
QQ-plots for all random effects (if model has more than one random
effect term).plot_model(type = "re") now supports standard errors
and confidence intervals for glmmTMB-objects.glmmTMB-tidier, which may have returned
wrong data for zero-inflation part of model.brms area now shown in each own facet per intercept.sjp.likert() for uneven
category count when neutral category is specified.plot_model(type = "int") could not automatically select
mdrt.values properly for non-integer variables.sjp.grpfrq() now correctly uses the complete space in
facets when facet.grid = TRUE.sjp.grpfrq(type = "boxplot") did not correctly label
the x-axis when one category had no elements in a vector.These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.