The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.
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.