sjPlot 2.8.9 2021-07-10

Bug fixes

  • Fixed issues from CRAN checks.

sjPlot 2.8.8 2021-05-25

Changes to functions

  • tab_model() now works properly with forthcoming parameters update.

Bug fixes

sjPlot 2.8.7 2021-01-10

Changes to functions

  • 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.

Bug fixes

  • Argument df.method in tab_model() did not accept all available options that were documented.
  • Fix CRAN check issues (warnings in new R-devel).

sjPlot 2.8.6 2020-10-28

Changes to functions

  • When dv.labels = "" in tab_model(), the row with names of dependent variables is omitted.

Bug fixes

  • Fix CRAN check issues (removed cross-references to archived packages).
  • The minus.sign argument in tab_model() now works.
  • show.std = TRUE in tab_model() did not exponentiate standardized coefficients for non-Gaussian models.

sjPlot 2.8.5 2020-09-24

Changes to functions

  • 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.

Bug fixes

  • Fixed issue with wrong n’s in plot_stackfrq() when weights were applied.
  • Fixed issue plot_stackfrq() when weights were applied and items should be sorted.
  • Fixed issue in plot_models() for models without intercept.
  • Fixed issue for wrong legend labelling in plot_models() when showing p-stars.
  • Fixed issue in plot_model() with type = "int" in detecting interaction terms when these were partly in parenthesis (like a * (b + c)).
  • Fixed issue in tab_model() with arguments show.stat = TRUE and show.std = TRUE, where the related statistic and CI columns for standardized coefficients were not shown.
  • Fixed issue in tab_model() for brmsfit models that did no longer show random effects information after the last update from the performance package.
  • Fixed issue with argument show.rownames in tab_df().

sjPlot 2.8.4 2020-05-24

Changes to functions

  • The robust estimation (argument 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().
  • The 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.

Bug fixes

  • Fixed bug in plot_xtab() with wrong order of legend labels.
  • Fixed bug in plot_models() with wrong axis title for exponentiated coefficients.
  • Fixed bug in tab_model() that did not show standard error of standardized coefficients when show.se = TRUE.

sjPlot 2.8.3 2020-03-09

General

Changes to functions

  • 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.

Bug fixes

  • Fixed issue in tab_model() and plot_model() for certain cases when coefficients could not be estimated and were NA.
  • Fixed issue in tab_model() with collapse.ci for Bayesian models.
  • Fixed issue in tab_model() when p.val="kr" and show.df=TRUE.
  • Fixed issue in tab_model() with formatting issues of p-values when standardized coefficients where requested.
  • Fixed issue in tab_model() due to changes in other packages sjPlot depends on.

sjPlot 2.8.2 2020-01-23

Function renaming

Changes to functions

  • Improved handling for tab_model() of robust estimation in general and Kenward-Roger or Satterthwaite approximations in particular for linear mixed models.
  • Revised code to cope with forthcoming tidyselect-update.

Bug fixes

  • Improved tab_df() now uses value labels for factors instead of numeric values.
  • Fixed some issues related to the lates brms-update.

sjPlot 2.8.1 2019-12-03

Changes to functions

  • tab_model() gets arguments bootstrap, iterations and seed to return bootstrapped estimates.

Bug fixes

  • Fixed issue in tab_model() with detecting labels when auto.label = TRUE.
  • Fixed issue in tab_model() for negative binomial hurdle mixed models (i.e. glmmTMB models with truncated negative-binomial family).
  • Fixed bug in tab_model() with show.reflvl = TRUE.
  • Fixed bug in tab_model() where labels for coefficients where not matching the correct coefficients.

sjPlot 2.8.0 2019-11-18

Breaking changes

  • Cluster functions have been removed, as these are now re-implemented in the parameters package.

General

Changes to functions

Bug fixes

  • sort.est = NULL in plot_model() now preserves original order of coefficients.
  • Fixed bug in automatic axis labelling for plot_frq() for non-labelled, numeric values.
  • Fixed bug in plot_frq() when plotting factors.
  • Arguments string.std_ci and string.std_se are no longer ignored in tab_model().

sjPlot 2.7.2 2019-09-29

General

  • Replaced performance::principal_component() by parameters::principal_component().
  • Fixed CRAN check issues, due to the latest bayestestR update.

Function renaming

Changes to functions

  • plot_grid() gets a tags-argument to add tags to plot-panels.

Bug fixes

  • Fixed bug in plot_stackfrq() for data frames with many missing values.
  • Fixed bug with sorting frequencies in plot_frq() when vector had more labels than values.
  • Fixed bug in tab_model() where show.reflvl = TRUE did not insert the reference category in first place, but in alphabetical order.

sjPlot 2.7.1 2019-09-07

General

  • Minor revisions to meet the changes in the forthcoming update from tidyr.
  • new color palettes were added (see show_sjplot_pals()).

Changes to functions

  • tab_model() now supports gamlss models.
  • tab_df() gets a digits argument, to round numeric values in output.

Bug fixes

  • Fixed bug in tab_model() with show.df = TRUE for lmerModLmerTest.
  • Fixed bug in tab_stackfrq() when items had different amount of valid values.

sjPlot 2.7.0 2019-08-02

Renamed functions

Changes to functions

plot_likert()

  • showed category labels in the top and bottom legends in two rows if there are more than six categories. Also, the categories are ordered column wise instead of row wise. This behaviour can now be controlled for grouped likert plots, using group.legend.options. The ordering now defaults to row wise and the user can force all categories onto a single row.
  • now automatically adjusts labels to avoid overlapping.

tab_model()

  • now supports wbm()-models from the panelr-package.
  • gets a show.aicc-argument to show the second order AIC.
  • gets a show.reflvl-argument to show the reference level of factors.
  • gets a string.std_se and string.std_ci-argument to change the column header for standard errors and confidence intervals of standardized coefficients.
  • no longer prints a message that default p-values for mixed models are based on Wald approximation.
  • 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.
  • The 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.

changes to other functions

  • plot_model() now supports wbm()-models from the panelr-package.

Bug fixes

  • plot_model(type = "int") now also recognized interaction terms with : in formula.
  • Argument string.est in tab_model() did not overwrite the default label for the estimate-column-header.
  • Minor fix in tab_model() for mixed models that can’t compute R2.
  • Fix issue in tab_model() when printing robust standard errors and CI (i.e. when using arguments vcov*).
  • The 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.
  • Argument wrap.labels in plot_frq() did not properly work for factor levels.
  • Fix issue in plot_models() that stopped for some models.
  • Fix issue in sjt.stackfrq(), when show.na = TRUE and some items had zero-values.

sjPlot 2.6.3 2019-04-27

General

Renamed functions

Changes to functions

tab_model()

  • 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.
  • The p.style-argument now also allows printing both numeric p-values and asterisks, by using p.style = "both".

plot_likert()

  • 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).
  • Argument 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.

Other

  • 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.

Bug fixes

  • Legend labels were inverted for brms-models in plot_model(..., type = "diag").
  • Legend labels were duplicated for marginal effects plots when color ="bw" and legend.title was specified.
  • Fixed encoding issues with help-files.
  • 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.

sjPlot 2.6.2 2018-12-18

General

  • Revised some help-files and vignettes.

Removed / Defunct

Following functions are now defunct:

  • sjt.lm(), sjt.glm(), sjt.lmer() and sjt.glmer(). Please use tab_model() instead.

Changes to functions

  • 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.
  • The rotation-argument in sjt.pca() and sjp.pca() now supports all rotations from psych::principal().

Bug fixes

  • 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.
  • If robust standard errors are requested in 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").
  • Fix issue in sjp.frq() with correct axis labels for non-labelled character vectors.

sjPlot 2.6.1 2018-10-14

General

  • Removed defunct functions.

Deprecated

  • sjt.lm(), sjt.glm(), sjt.lmer() and sjt.glmer() are now deprecated. Please use tab_model() instead.

Changes to functions

  • Arguments 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.

Bug fixes

  • Fixed bug from the last update that made value labels disappear for plot_likert().
  • tab_model() now also accepts multiple model-objects stored in a list as argument, as stated in the help-file.
  • The file-argument now works again in sjt.itemanalysis().
  • Argument show.ci in tab_model() did not compute confidence intervals for different levels.

sjPlot 2.6.0 2018-08-23

General

  • 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.
  • Reduce package dependencies.

Renamed functions

Changes to functions

  • Improved support for 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.
  • Argument hide.progress in view_df() is deprecated. Please use verbose now.
  • The statistics-argument in sjt.xtab() gets a "fisher"-option, to force Fisher’s Exact Test to be used.

Removed / Defunct

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.

Bug fixes

  • Due to changes in the broom and lmerTest packages, tidiers did no longer work for lmerModLmerTest objects.
  • Fix issue with standardized coefficient (argument show.std) in tab_model().

sjPlot 2.5.0 2018-07-12

New functions

Deprecated

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.

Removed / Defunct

Following functions are now defunct:

Changes to functions

  • 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.
  • Diagnostic plots in plot_model() (type = "diag") can now also be used with brmsfit-objects.
  • Axis limits of diagnostic plots in plot_model() (type = "diag") for Stan-models (brmsfit or stanreg resp. stanfit) can now be set with the axis.lim-argument.
  • The 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.
  • Better default calculation for grid breaks in plot_model() and plot_models() when the grid.breaks-argument is of length one.
  • The 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]".
  • For coefficient-plots, the 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).

Bug fixes

  • plot_model(type = "re") now supports standard errors and confidence intervals for glmmTMB-objects.
  • Fixed typo for glmmTMB-tidier, which may have returned wrong data for zero-inflation part of model.
  • Multiple random intercepts for multilevel models fitted with brms area now shown in each own facet per intercept.
  • Remove unnecessary warning in 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.
  • Problems with German umlauts when printing HTML tables were fixed.