NEWS.md
style = "distribution"
. Thanks for Indrajee @IndrajeetPatil pointing that out.model_order
in dwplot
to allow customizing the order of models to present.fontSize
in add_brackets
to allow customizing the font size of bracket labels, and opening possibility for users to further customize bracket labels.parameters
instead of broomExtra
as the plotting data frame creator. Thanks for the suggestion from @IndrajeetPatil.dw_tidy
engine to broomExtra::tidy_parapmeter
. Thanks for Indrajeet Patil’s amazing package.margins::margins
.The vline
argument is now available for dwplot()
. Passing a geom_vline()
object to this argument, typically one with xintercept = 0
, will plot this line behind the plotted coefficients, which most will find aesthetically preferable. The default for this argument is NULL
, so if you prefer not to include such lines or just like them plotted last and foremost, there’s no need to change your code.
dwplot()
now again accepts the whisker_arg
argument to change the appearance of the whiskers representing the confidence intervals that has been lost since v0.3.0. This means you can, for example, specify different colors for the dots and the whiskers:
# load the library
library(dotwhisker)
#> Loading required package: ggplot2
# linear model of interest
lm_object <- stats::lm(formula = wt ~ am * cyl, data = mtcars)
# creating the plot with dwplot
dwplot(x = lm_object,
dot_args = list(color = "red"), # color for the dot
whisker_args = list(color = "black"), # color for the whisker
vline = ggplot2::geom_vline(xintercept = 0, # put vline _behind_ coefs
colour = "grey60",
linetype = 2,
size = 1))
Created on 2018-06-27 by the reprex package (v0.2.0).
add_brackets()
that caused brackets to overlap in large models or when many models were included in a single plot.style = "distribution"
in the arguments to dwplot()
presents regression coefficients as normal distributions, underscored with a line representing the desired confidence interval.relabel_predictors()
now conveniently reorders the predictors as well.add_brackets()
can now be added directly to the end of a chain of commands that generate a dotwhisker plot; the intermediate object necessary in past versions is no longer needed. Just wrap the plotting commands in braces ({
and }
) before piping them to add_brackets()
!alpha
argument to dwplot()
should no longer be used to change the width of confidence intervals; use conf.int
(to be passed to broom::tidy
via ...
) instead.dwplot()
is passed model objects rather than a tidy data frame, the regression coefficients are now rescaled by two standard deviations of their respective variables in the analyzed data (per by_2sd()
) by default. This may be changed by setting by_2sd = FALSE
.add_brackets()
that de-centered the brackets
dot_args
to be ignored after plots were passed to relabel_predictors()
small_multiple()
from directly reading confidence intervals from a model.by_2sd()
now adjusts, if present, any confidence intervals in tidy data frames passed to the function.Thanks to Steven V. Miller and Ryan Burge for bug reports, and to Ben Edwards and Jay Jacobs for inspiring style = "distribution"
!
relabel_predictors
. relabel_predictors
now accepts plots as well as tidy dataframes as input; that is, it may now be used both before and after calls to dwplot
.relabel_y_axis
. It is easy to mistakenly mislabel variables with relabel_y_axis
, and it has a conflict with add_brackets
in single-model plots.dwplot
works for polr
projects.dplyr::group_by
show_intercept
argument.devtools
suggests.gridExtra
.ggplot2
.