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.