NEWS.md
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.save_plot()
function.sjt.grpmean()
is now deprecated. Please use sjstats::grpmean()
with argument out = "viewer"
instead.sjt.mwu()
is now deprecated. Please use sjstats::mwu()
with argument out = "viewer"
instead.sjt.df()
is now deprecated. Please use sjmisc::descr()
with argument out = "viewer"
or tab_df()
instead.plot_model()
now also supports clm
-models from package ordinal, polr
-models from package MASS, multinom
-models from package nnet and Zelig-relogit
-models from package Zelig.plot_model()
gets a show.legend
-argument to show or hide the legend for marginal effects plots.plot_model()
gets a se
-argument to plot (robust) standard errors instead of confidence intervals for coefficient-plots.plot_model()
(type = "diag"
) now also plot diagnostics of random effects from (generalized) linear mixed models....
-argument of plot_model()
now also accepts the arguments sep_in
and sep_out
, which are passed down to snakecase::to_any_case()
for case conversion of term labels (axis labels).title
-argument in plot_model()
now also works for plotting random effects (type = "re"
).sjt.itemanalysis()
no longer returns a list of score items, but only a data frame of scores.sjp.grpfrq()
gets a show.ci
-argument to add notches to boxplots.view_df()
did not work with double values (with decimal points) when show.values = TRUE
.view_df()
caused an error when a variable has completely missing values.plot_models()
did not properly remove intercepts from output for survey models, when show.intercept = FALSE
.plot_models()
did not automatically transform axis for all applicable model types.get_model_data()
did not work for marginal effects plots.sjt.grpmean()
, resulting in multiple table outputs and a wrong overall p-value in the summary line.plot_model()
.sjp.likert()
did not show correct order for factors with character levels, when a neutral category was specified and was not the last factor level.type = "re"
) for specific brms
-models.set_theme()
was removed. Instead, there are some new predifined themes available (see ?"sjPlot-themes"
). The former sjp.setThemes()
was renamed to set_theme()
instead.plot_model()
as replacement for sjp.lm()
, sjp.glm()
, sjp.lmer()
, sjp.glmer()
and sjp.int()
(which will become deprecated in the future, and will later be removed).get_model_data()
to get the data from plot-objects created with plot_model()
.font_size()
, label_angle()
and legend_style()
as convenient ways to tweak common ggplot-theme-elements.view_df()
now better handles string variables and gets a show.string.values
-argument to omit the output of values from string variables.view_df()
gets a max.len
-argument to truncate output for variables with many values.view_df()
displays more information on non-labelled, numeric variables.sjp.pca()
and sjt.pca()
now give more informative error messages when just one component is extracted.rm.terms
-argument in plot_models()
.view_df()
did not work for string variables with missing values.sjt.pca()
as file.sjp.xtab()
did not work when show.n
and show.prc
were set to FALSE
, but show.values
was TRUE
.sjt.*
-functions) displayed in the viewer pane now automatically add a CSS-style for white page background. This fixes an RStudio issue on OS X, where the new look’n’feel used dark backgrounds in the viewer pane, making output hardly readable.plot_models()
as replacement for sjp.lmm()
and sjp.glmm()
(which are now deprecated).sjp.fa()
and sjt.fa()
to plot or print as table the results of factor analyses.sjt
-functions can now be directly integrated into knitr-code-chunks, because sjPlot exports a knitr-print-method (see vignette("sjtbasic", "sjPlot")
).sjtab()
now also works within knitr-documents (see vignette("sjtbasic", "sjPlot")
).save_plot()
.save_plot()
now also supports svg-format.type = "eff"
), the axis.title
-argument can now be used to change the title of y-axes.sjp.lm()
, sjp.glm()
, sjp.lmer()
and sjp.glmer()
, if color palette has more values than needed, it is silently shortend to the required length.geom.colors
now also applies to plot-type type = "ri.slope"
.sjt.corr()
and sjp.corr()
is now pearson
.emph.p
for printing tables of regression models now defaults to FALSE
.sjt.frq()
for variables with many missing values and labelled values that did not occur on that variable.value.labels
had no effect for sjt.frq()
.sjt.grpmean()
sometimes not worked for factors without variable labels.sjp.glm()
used Odds Ratios as default title for y-axis when plotting marginal effects. Fixed, now y-axis is correctly labelled.sjt.glm()
used “Odds Ratios” as default column heading for the estimates, even for poisson or other models. Now the string for column headers is selected based on the first model input of the function.type = "pred"
) for categorical variables on the x-axis.geom.colors = "bw"
for linetype-plots, to create black & white figures that use different linetypes instead of different colors.sjp.kfold_cv()
now also supports poisson and negative binomial regression models.sjp.pca()
and sjt.pca()
get a rotation
-argument, to use either varimax- or oblimin-transformation of factor loadings.show.value
now also applies to bar plots in sjp.pca()
.sjt.glm()
, for generalized linar (mixed) models, now shows adjusted standard errors, using the Taylor series-based delta method.sjt.xtab()
, sjp.xtab()
and sjp.grpfrq()
.sjt.xtab()
is now dentoted as V.sjt.xtab()
gets a ...
-argument, to pass down further arguments to the test statistics functions chisq.test()
and fisher.test()
.sjt.xtab()
gets a statistics
-argument, to select one of different measures of associations for the table summary.