The dlvPlot function produces a dot-violin-line plot, and dlvTheme is the default theme.

```
dlvTheme(base_size = 11, base_family = "", ...)
dlvPlot(
dat,
x = NULL,
y,
z = NULL,
conf.level = 0.95,
jitter = "FALSE",
binnedDots = TRUE,
binwidth = NULL,
error = "lines",
dotsize = "density",
singleColor = "black",
comparisonColors = rosetta::opts$get("dlvPlotCompCols"),
densityDotBaseSize = 3,
normalDotBaseSize = 1,
violinAlpha = 0.2,
dotAlpha = 0.4,
lineAlpha = 1,
connectingLineAlpha = 1,
meanDotSize = 5,
posDodge = 0.2,
errorType = "both",
outputFile = NULL,
outputWidth = 10,
outputHeight = 10,
ggsaveParams = list(units = "cm", dpi = 300, type = "cairo")
)
# S3 method for dlvPlot
print(x, ...)
```

- base_size, base_family, ...
Passed on to the ggplot theme_grey() function.

- dat
The dataframe containing x, y and z.

- x
Character value with the name of the predictor ('independent') variable, must refer to a categorical variable (i.e. a factor).

- y
Character value with the name of the critetion ('dependent') variable, must refer to a continuous variable (i.e. a numeric vector).

- z
Character value with the name of the moderator variable, must refer to a categorical variable (i.e. a factor).

- conf.level
Confidence of confidence intervals.

- jitter
Logical value (i.e. TRUE or FALSE) whether or not to jitter individual datapoints. Note that jitter cannot be combined with posDodge (see below).

- binnedDots
Logical value indicating whether to use binning to display the dots. Overrides jitter and dotsize.

- binwidth
Numeric value indicating how broadly to bin (larger values is more binning, i.e. combining more dots into one big dot).

- error
Character value: "none", "lines" or "whiskers"; indicates whether to show the confidence interval as lines with (whiskers) or without (lines) horizontal whiskers or not at all (none)

- dotsize
Character value: "density" or "normal"; when "density", the size of each dot corresponds to the density of the distribution at that point.

- singleColor
The color to use when drawing one or more univariate distributions (i.e. when no

`z`

is specified.- comparisonColors
The colors to use when a

`z`

is specified. This should be at least as many colors as`z`

has levels. By default, palette`Set1`

from`RColorBrewer`

is used.- densityDotBaseSize
Numeric value indicating base size of dots when their size corresponds to the density (bigger = larger dots).

- normalDotBaseSize
Numeric value indicating base size of dots when their size is fixed (bigger = larger dots).

- violinAlpha
Numeric value indicating alpha value of violin layer (0 = completely transparent, 1 = completely opaque).

- dotAlpha
Numeric value indicating alpha value of dot layer (0 = completely transparent, 1 = completely opaque).

- lineAlpha
Numeric value indicating alpha value of the confidence interval line layer (0 = completely transparent, 1 = completely opaque).

- connectingLineAlpha
Numeric value indicating alpha value of the layer with the lines connecting the means (0 = completely transparent, 1 = completely opaque).

- meanDotSize
Numeric value indicating the size of the dot used to indicate the mean in the line layer.

- posDodge
Numeric value indicating the distance to dodge positions (0 for complete overlap).

- errorType
If the error is shown using lines, this argument indicates Whether the errorbars should show the confidence interval (

`errorType='ci'`

), the standard errors (`errorType='se'`

), or both (`errorType='both'`

). In this last case, the standard error will be wider than the confidence interval.- outputFile
A file to which to save the plot.

- outputWidth, outputHeight
Width and height of saved plot (specified in centimeters by default, see

`ggsaveParams`

).- ggsaveParams
Parameters to pass to ggsave when saving the plot.

The behavior of this function depends on the arguments.

If no x and z are provided and y is a character value, dlvPlot produces a univariate plot for the numerical y variable.

If no x and z are provided, and y is c character vector, dlvPlot produces multiple Univariate plots, with variable names determining categories on x-axis and with numerical y variables on y-axis

If both x and y are a character value, and no z is provided, dlvPlot produces a bivariate plot where factor x determines categories on x-axis with numerical variable y on the y-axis (roughly a line plot with a single line)

Finally, if x, y and z are each a character value, dlvPlot produces multivariate plot where factor x determines categories on x-axis, factor z determines the different lines, and with the numerical y variable on the y-axis

An object is returned with the following elements:

- dat.raw
Raw datafile provided when calling dlvPlot

- dat
Transformed (long) datafile dlvPlot uses

- descr
Dataframe with extracted descriptives used to plot the mean and confidence intervals

- yRange
The range of the Y variable used to construct the plot

- plot
The plot itself

This function creates Dot Violin Line plots. One image says more than a thousand words; I suggest you run the example :-)

```
### Note: the 'not run' is simply because running takes a lot of time,
### but these examples are all safe to run!
if (FALSE) {
### Create simple dataset
dat <- data.frame(x1 = factor(rep(c(0,1), 20)),
x2 = factor(c(rep(0, 20), rep(1, 20))),
y=rep(c(4,5), 20) + rnorm(40));
### Generate a simple dlvPlot of y
dlvPlot(dat, y='y');
### Now add a predictor
dlvPlot(dat, x='x1', y='y');
### And finally also a moderator:
dlvPlot(dat, x='x1', y='y', z='x2');
### The number of datapoints might be a bit clearer if we jitter
dlvPlot(dat, x='x1', y='y', z='x2', jitter=TRUE);
### Although just dodging the density-sized dots might work better
dlvPlot(dat, x='x1', y='y', z='x2', posDodge=.3);
}
```