Collection of plotting and table output functions for data visualization. Results of various statistical analyses (that are commonly used in social sciences) can be visualized using this package, including simple and cross tabulated frequencies, histograms, box plots, (generalized) linear models, mixed effects models, principal component analysis and correlation matrices, cluster analyses, scatter plots, stacked scales, effects plots of regression models (including interaction terms) and much more. This package supports labelled data.
Collection of plotting and table output functions for data visualization. Results of various statistical analyses (that are commonly used in social sciences) can be visualized using this package, including simple and cross tabulated frequencies, histograms, box plots, (generalized) linear models, mixed effects models, PCA and correlation matrices, cluster analyses, scatter plots, Likert scales, effects plots of interaction terms in regression models, constructing index or score variables and much more.
To install the latest development snapshot (see latest changes below), type following commands into the R console:
Please note that the latest development snapshot most likely depends on the latest build of the sjmisc-package, so you probably want to install it as well:
To install the latest stable release from CRAN, type following command into the R console:
In case you want / have to cite my package, please use
citation('sjPlot') for citation information. Since core functionality of package depends on the ggplot-package, consider citing this package as well.
sjp.kfold_cv()to plot model fit from k-fold cross-validation.
scatter.plotwas renamed to
sjt.frq()was renamed to
sjtab()also accept grouped data frames, to create plots or tables for all subgroups.
type = "pred",
type = "slope",
type = "pred.fe"and
type = "fe.slope"can now also plot data points when
show.scatter = TRUE. Use
point.alphato adjust alpha-level of data points.
type = "pred"and
type = "pred.fe"now plot error bars for
show.ci = TRUEand a discrete variable on the x-axis.
type = "pred"and
type = "pred.fe"now accept three variables for the
vars-argument, to facet grouped predictions by a third variable.
...-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.
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.
robust-argument to compute robust standard errors and confidence intervals.
sjp.resid()now also returns a plot with the residual pattern,
axis.titleargument. 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.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).
sjp.frq()showed messed up labels when a labelled vector had both
NaNor infinite values.
sjtab()did not create tables for
fun = "xtab"with additional arguments.
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".
sjplot, a pipe-friendly wrapper for some of this package's plotting-functions.
sjtab, a pipe-friendly wrapper for some of this package's table-functions.
sjp.resid, an experimental function to plot and analyze residuals from linear models.
plot_gridto plot a list of ggplot-objects as arranged grid in a single plot.
set_themeto use a preset of default themes for plots from the sjp-functions.
show.cinow 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.glmershould 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
sjp.setThemeno longer sets default theme presets for plots; use
type = "std".
type = "eff") for
sjp.glmerdid not plot all predictors, when predictor name was not exactly specified in formula, but transformed inside formula (e.g.
log(pred + 1)).