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.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() 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]".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.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.sjp.lm() for type = "ma" now uses subtitles in multi-line plot-titles.sjp.kfold_cv() had wrong leading sign (i.e. positive residuals were negative and vice versa).sjp.kfold_cv() to plot model fit from k-fold cross-validation.scatter.plot was renamed to show.scatter.var.labels in sjt.frq() was renamed to title.sjplot() and sjtab() also accept grouped data frames, to create plots or tables for all subgroups.sjp.glm() and sjp.glmer(), type = "pred", type = "slope", type = "pred.fe" and type = "fe.slope" can now also plot data points when show.scatter = TRUE. Use point.alpha to adjust alpha-level of data points.sjp.lm(), sjp.lmer(), sjp.glm() and sjp.glmer(), type = "pred" and type = "pred.fe" now plot error bars for show.ci = TRUE and a discrete variable on the x-axis.sjp.glm() and sjp.glmer(), type = "pred" and type = "pred.fe" now accept three variables for the vars-argument, to facet grouped predictions by a third variable.sjp.lm(), sjp.lmer(), sjp.glm() and sjp.glmer(), the ...-ellipses argument now is also passed down to all errorbars- and smooth-geoms in prediction- and effect-plots, so you can now use the width-argument to show the small stripes at the lower/upper end of the error bars, the alpha-argument to define alpha-level or the level-argument to define the level of confidence bands.sjp.lm(), sjp.lmer(), sjp.glm() and sjp.glmer() get a point.color-argument, do define color of point-geoms when show.scatter = TRUE. If not defined, point-geoms will have same group-color as lines.type = "eff") now plot data points for discrete variables on the x-axis.sjt.lm() and sjt.glm() get a robust-argument to compute robust standard errors and confidence intervals.sjp.resid() now also returns a plot with the residual pattern, $pattern.title or axis.title argument. Use a character vector of length > 1 to define (axis) titles for each plot or facet; use "" to remove the titles.geom.size-argument for histogram and density plots in sjp.frq().sjp.xtab() and sjp.grpfrq() for stacked bars (position_stack() reversed order since last ggplot2-update), so labels are now correclty positioned again.sjp.likert(), so groups are now in correct order again.sjt.grpmean() for variables with unused value labels (values that were labelled, but did not appear on the vector).show.summary-argument in sjt.xtab().sjt.frq() and sjp.frq() showed messed up labels when a labelled vector had both NA values and NaN or infinite values.sjtab() did not create tables for fun = "xtab" with additional arguments.sjp.int(), sjp.glm() and sjp.glmer() now support the transformation-argument from the effects-package. For example, when calling sjp.glm(fit, type = "eff", transformation = NULL), predictions are on their original scale (y-scale) and the title for the y-scale is changed accordingly.sjp.stackfrq(), which were reversed by the last ggplot2-update, where position_stack() now sort the stacking order to match grouping order.sjplot() that caused figures not being plotted in certain situations.sjp.lmm(), which caused an error for plotting multiple mixed models when Intercept was hidden.sjp.lmm(), which caused an error for plotting multiple mixed models when type = "std" or type = "std2".sjp.resid, an experimental function to plot and analyze residuals from linear models.plot_grid to plot a list of ggplot-objects as arranged grid in a single plot.set_theme to use a preset of default themes for plots from the sjp-functions.sjp.glmer and sjp.lmer, argument show.ci now also applies for plotting random effects (type = "re", the default), so confidence intervals may not be calculated. This may be useful in some cases where computation of standard errors for random effects caused an error.type = "eff") for sjp.lm, sjp.glm, sjp.lmer and sjp.glmer should now better handle categorical variables and their labels, including using error bars insted of regions for confidence intervals.table(*, exclude = NULL) was changed to table(*, useNA = "always"), because of planned changes in upcoming R version 3.4.get_option("p_zero") was removed, and sjt.lm, sjt.glm, sjt.lmer and sjt.glmer get a p.zero argument.sjp.setTheme no longer sets default theme presets for plots; use set_theme instead.