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Overlapping density distributions for two variables

Source: R/eda_dens.R
eda_dens.Rd

eda_dens generates overlapping density distributions for two variables.

Usage

eda_dens(
  x,
  y,
  fac = NULL,
  p = 1L,
  tukey = FALSE,
  base = exp(1),
  fx = NULL,
  fy = NULL,
  grey = 0.6,
  col = "red",
  show.par = TRUE,
  alpha = 0.4,
  xlab = NULL,
  ylab = NULL,
  xaxis = NULL,
  switch = FALSE,
  legend = TRUE,
  kernel = "gaussian",
  ...
)

Arguments

x

A numeric vector for the first variable, or a dataframe when providing data in long format.

y

A numeric vector for the second variable. When x is a dataframe, y should be the unquoted name of the column containing the continuous numeric values.

fac

When x is a dataframe, fac should be the unquoted name of the column containing the two-level categorical (grouping) variable.

p

Power transformation to apply to both sets of values.

tukey

Boolean determining if a Tukey transformation should be adopted (FALSE adopts a Box-Cox transformation).

base

Base used with the log() function if p = 0.

fx

Formula to apply to x variable. This is computed after any transformation is applied to the x variable.

fy

Formula to apply to y variable. This is computed after any transformation is applied to the y variable.

grey

Grey level to apply to plot elements (0 to 1 with 1 = black).

col

Fill color for second density distribution.

show.par

Boolean determining if parameters such as power transformation or formula should be displayed.

alpha

Fill transparency (0 = transparent, 1 = opaque). Only applicable if rgb() is not used to define fill colors.

xlab

X variable label. Ignored if x is a dataframe. This is not the x-axis label!

ylab

Y variable label. Ignored if x is a dataframe. This is not the y-axis label!

xaxis

Label for x-axis. Defaults to the name of the fac column when using a dataframe, and to "Value" when using vectors.

switch

Boolean determining if the axes should be swapped. Only applies to dataframe input. Ignored if vectors are passed to the function.

legend

Boolean determining if a legend should be added to the plot.

kernel

The kernel to be used. Can be one of the kernels supported by stats::density ("gaussian", "epanechnikov", "rectangular", "triangular", "biweight", "cosine", "optcosine"), or "sliding" for a custom sliding-window rectangular kernel estimator.

...

Arguments passed to the stats::density() function (e.g., bw, n). These are also used by the "sliding" kernel.

Value

Does not return a value.

Details

This function will generate overlapping density plots with the first variable assigned a grey color and the second variable assigned the default red color.

The "sliding" kernel implements a simple sliding window estimator with a rectangular kernel. The density at a point x0 is computed as the proportion of data points falling within the window divided by the bandwidth and sample size product (# points in bin / (bandwidth * sample size).

Examples


 # Passing data as two separate vector objects
 set.seed(207)
 x <- rbeta(1000,2,8)
 y <- x * 1.5 + 0.1
 eda_dens(x, y)


 # Passing data as a dataframe
 dat <- data.frame(val = c(x, y),
                   grp = c(rep("x", length(x)), rep("y", length(y))))
 eda_dens(dat, val, grp)


 # Defining density paramters that are passed to stats::density()
 eda_dens(x,y, bw = 0.05, kernel="cosine", n = 10)


 # Adopting the custom sliding rectangler kernel
 eda_dens(x, y, from = 0, to=1, kernel = "sliding", bw=0.2, n = 10)