plot.dabest_effsize: Create an Estimation Plot
In dabestr: Data Analysis using Bootstrap-Coupled Estimation

Description Usage Arguments Value References See Also Examples

An estimation plot has two key features.

It presents all datapoints as a swarmplot or sinaplot, which orders each point to display the underlying distribution.
It presents the effect size as a bootstrap 95 percent confidence interval on a separate but aligned axes.

Estimation plots emerge from estimation statistics, an intuitive framework that avoids the pitfalls of significance testing. It uses familiar statistical concepts: means, mean differences, and error bars. More importantly, it focuses on the effect size of one's experiment/intervention, as opposed to a false dichotomy engendered by P values. This function takes the output of the effect size functions and produces an estimation plot.

## S3 method for class 'dabest_effsize'
plot(
  x,
  ...,
  color.column = NULL,
  palette = "Set1",
  float.contrast = TRUE,
  slopegraph = TRUE,
  group.summaries = "mean_sd",
  rawplot.type = c("swarmplot", "sinaplot"),
  rawplot.ylim = NULL,
  rawplot.ylabel = NULL,
  rawplot.markersize = 2,
  rawplot.groupwidth = 0.3,
  effsize.ylim = NULL,
  effsize.ylabel = NULL,
  effsize.markersize = 4,
  theme = ggplot2::theme_classic(),
  tick.fontsize = 11,
  axes.title.fontsize = 14,
  show.legend = TRUE,
  swarmplot.params = NULL,
  sinaplot.params = NULL,
  slopegraph.params = NULL
)

`x`	A `dabest_effsize` object, generated by one of the effect size functions currently available in `dabestr`.
`...`	Signature for S3 generic function.
`color.column`	default `NULL`. This is a column in the data.frame passed to the `dabest` function. This column will be treated as a factor and used to color the datapoints in the rawdata swarmplot.
`palette`	default "Set1". Accepts any one of the RColorBrewer palettes, or a vector of colors. Colors can be specified as RGB hexcode or as a named color. See the "Palettes" section in scale_color_brewer for more on palettes. To obtain all 657 named colors in R, enter `colors()` at the console.
`float.contrast`	default `TRUE`. If `idx` in the `dabest` object contains only 2 groups, `float.contrast = TRUE` will plot the effect size and the bootstrap confidence interval in a horizontally-aligned axes (also known as a Gardner-Altman plot).
`slopegraph`	boolean, default `TRUE`. If the `dabest` object contains paired comparisons, `slopegraph = TRUE` will plot the rawdata as a Tufte slopegraph.
`group.summaries`	"mean_sd", "median_quartiles", or `NULL`. Plots the summary statistics for each group. If 'mean_sd', then the mean and standard deviation of each group is plotted as a gapped line beside each group. If 'median_quartiles', then the median and 25th & 75th percentiles of each group is plotted for each group as a gapped line. If `group.summaries = NULL`, the summaries are not shown.
`rawplot.type`	default "beeswarm". Accepts either "beeswarm" or "sinaplot". See geom_quasirandom and geom_sina for more information.
`rawplot.ylim`	default `NULL`. Enter a custom y-limit for the rawdata plot. Accepts a vector of length 2 (e.g. c(-50, 50)) that will be passed along to coord_cartesian.
`rawplot.ylabel`	default `NULL`. Accepts a string that is used to label the rawdata y-axis. If `NULL`, the column name passed to `y` is used.
`rawplot.markersize`	default 2. This is the size (in points) of the dots used to plot the individual datapoints. There are 72 points in one inch. See this article for more info.
`rawplot.groupwidth`	default 0.3. This is the maximum amount of spread (in the x-direction) allowed, for each group.
`effsize.ylim`	default `NULL`. Enter a custom y-limit for the effect size plot. This parameter is ignored if `float.contrast = TRUE`. Accepts a vector of length 2 (e.g. `c(-50, 50)`) that will be passed along to coord_cartesian.
`effsize.ylabel`	default `NULL`. Accepts a string that is used to label the effect size y-axis. If `NULL`, this axes will be labeled "(un)paired <effect size>", where effect size is the effect size function used to generate the `dabest_effsize` object currently being plotted.
`effsize.markersize`	default 4. This is the size (in points) of the dots used to indicate the effect size.
`theme`	default theme_classic. This is the `ggplot2` theme that is used to style various elements of the estimation plot.
`tick.fontsize`	default 11. This controls the font size (in points) of all tick labels.
`axes.title.fontsize`	default 14. This determines the font size (in points) of the axes titles.
`show.legend`	boolean, default `TRUE`. If `FALSE`, the color legend will not be displayed, even if `color.column` is supplied.
`swarmplot.params`	default `NULL`. Supply list of `keyword = value` pairs to geom_quasirandom.
`sinaplot.params`	default `NULL`. Supply list of `keyword = value` pairs to `ggforce::geom_sina()`.
`slopegraph.params`	default `NULL`. Supply list of `keyword = value` pairs to `ggplot2`'s `geom_line()`. This controls the appearance of the lines plotted for a paired slopegraph.

A ggproto object. This object is actually composed of two ggplot2 objects (one for the rawdata swarmplot or slopegraph, another for the effect sizes and bootstrapped confidence intervals). These are arranged in the desired configuration (whether as a Gardner-Altman plot or a Cumming plot) by the plot_grid() function in the cowplot package.

Moving beyond P values: Data analysis with estimation graphics. Nature Methods 2019, 1548-7105. Joses Ho, Tayfun Tumkaya, Sameer Aryal, Hyungwon Choi, Adam Claridge-Chang

Loading data for effect size computation.
Effect size computation from the loaded data.

Run vignette("Using dabestr", package = "dabestr") in the console to read more about using parameters to control the plot features.

# Performing unpaired (two independent groups) analysis.
# We want to obtain the mean difference between the petal widths
# of setosa and versicolor species.
unpaired_mean_diff <- dabest(iris, Species, Petal.Width,
                             idx = c("setosa", "versicolor"),
                             paired = FALSE) %>%
                       mean_diff()

# Create a Gardner-Altman estimation plot.
plot(unpaired_mean_diff)


## Not run: 
# Comparing versicolor and virginica petal widths to setosa petal width.
shared_control_data <- dabeıst(iris, Species, Petal.Width,
                              idx = c("setosa", "versicolor", "virginica")) %>%
                       mean_diff()

# Create a Cumming estimation plot.
plot(shared_control_data)

## End(Not run)