eda_normfit: Normal fit vs density plot.

In mgimond/tukeyedar: Tukey Inspired Exploratory Data Analysis Functions

Normal fit vs density plot.

Description

eda_normfit generates a fitted Normal distribution to the data with the option to compare it to a density distribution.

Usage

eda_normfit(
  dat,
  x = NULL,
  grp = NULL,
  p = 1,
  tukey = FALSE,
  show.par = TRUE,
  sq = TRUE,
  inner = 0.6826,
  dens = TRUE,
  bw = "SJ-dpi",
  kernel = "gaussian",
  pch = 16,
  size = 0.8,
  alpha = 0.3,
  p.col = "grey50",
  p.fill = "grey80",
  grey = 0.7,
  col.ends = "grey90",
  col.mid = "#EBC89B",
  col.ends.dens = "grey90",
  col.mid.dens = "#EBC89B",
  offset = 0.02,
  tsize = 1.5,
  xlab = NULL,
  ylab = NULL,
  ...
)

Arguments

dat	Vector of values, or a dataframe.
x	Column of values if dat is a dataframe, ignored otherwise.
grp	Column of grouping variables if dat is a dataframe, ignored otherwise.
p	Power transformation to apply to all values.
tukey	Boolean determining if a Tukey transformation should be adopted (TRUE) or if a Box-Cox transformation should be adopted (FALSE).
show.par	Boolean determining if the power transformation used with the data should be displayed in the plot's upper-right corner.
sq	Boolean determining if the plot should be square.
inner	Fraction of values that should be captured by the inner color band of the normal and density plots. Defaults to 0.6826 (inner 68% of values).
dens	Boolean determining if the density plot should be displayed alongside the Normal fit plot.
bw	Bandwidth parameter passed to the density() function.
kernel	Kernel parameter passed to the density() function.
pch	Point symbol type.
size	Point side.
alpha	Fill transparency (0 = transparent, 1 = opaque). Only applicable if rgb() is not used to define fill colors.
p.col	Color for point symbol.
p.fill	Point fill color passed to bg (Only used for pch ranging from 21-25).
grey	Grey level to apply to plot elements such as axes, labels, etc... (0 to 1 with 1 = black).
col.ends	Fill color for ends of the Normal distribution.
col.mid	Fill color for middle band of the Normal distribution.
col.ends.dens	Fill color for ends of the density distribution.
col.mid.dens	Fill color for middle band of the density distribution.
offset	A value (in x-axis units) that defines the gap between left and right side plots. Ignored if dens is FALSE.
tsize	Size of plot title.
xlab	X variable label.
ylab	Y variable label.
...	Note used.

Details

This function will generate a (symmetrical) Normal distribution fitted to the data if dens is set to FALSE or a side-by-side density/Normal fit plot if dens is set to TRUE. If the latter, the density plot will be on the left side and the Normal fit will be on the right side of the vertical axis.

The plots have two fill colors: one for the inner band and the other for the outer band. The inner band shows the area of the curve that encompasses the desired fraction of values defined by inner. By default, this value is 0.6826, or 68.26% (this is roughly the percentage of values covered by +/- 1 standard deviations of a Normal distribution). For the Normal fit plot, the range is computed from the theoretical Normal and not from the actual values. For the density plot, the range is computed from the actual values.

If a density plot is desired, dens = TRUE, a gap (defined by offset) is created between the left side density plot and the right side Normal fit plot. This function makes use of the built-in stats::density function. As such, you can pass the bw and kernel parameters as you would for the density() function. Points showing the location of values along the y-axis are also added to help view their distributions relative to the density and Normal fit curves.

Measures of centrality are computed differently for the Normal fit and density plots. The mean is computed for the Normal fit plot and the median is computed for the density plot. These measures of centrality are shown as black horizontal lines in the plot.

The areas under the density and Normal fit plots are scaled to their peak values, respectively. So, the areas should not be compared between both distributions.

Value

Does not return a value.

See Also

density

Examples

# Explore a skewed distribution
set.seed(218)
x <- rexp(500)

# Generate base histogram
hist(x)

# Plot density/Normal fit plot
eda_normfit(x)
eda_normfit(x)

# Limit the plot to just a Normal fit
eda_normfit(x, dens = FALSE)

# The inner band's range can be modified. Here, we view the interquartile
# range, the +/- 1 standard deviation range and the inner 95% range)
OP <- par(mfrow = c(1,3))
invisible(sapply(c(0.5, 0.6826, 0.95),
      function(prop) eda_normfit(x, inner = prop, tsize = 1,
                                 ylab = paste(prop*100,"% of values"))))
par(OP)

# The bandwidth selector can also be specified
OP <- par(mfrow=c(2,3))
 invisible(sapply(c("SJ-dpi", "nrd0", "nrd", "SJ-ste", "bcv", "ucv" ),
       function(band) eda_normfit(x, bw = band, tsize=0.9, size=0, offset=0.005,
                                  ylab = band)))
par(OP)

# The bandwidth argument can also be passed a numeric value
OP <- par(mfrow=c(1,3))
invisible(sapply(c(0.2, 0.1, 0.05 ),
       function(band) eda_normfit(x, bw = band, tsize=1,size=.5, offset=0.01,
                                  ylab = band)))
par(OP)

# Examples of a few kernel options
OP <- par(mfrow=c(1,3))
invisible(sapply(c("gaussian", "optcosine", "rectangular" ),
      function(k) eda_normfit(x, kernel = k, tsize=1, size=.5, offset=0.01,
                               ylab = k)))
par(OP)

# Another example where data are passed as a dataframe
set.seed(540)
dat <- data.frame(value = rbeta(20, 1, 50),
                 grp = sample(letters[1:3], 100, replace = TRUE))
eda_normfit(dat, value, grp)

# Points can be removed and the gap rendered narrower
eda_normfit(dat, value, grp, size = 0, offset = 0.01)

# Color can be modified. Here we modify the density plot  fill colors
eda_normfit(dat, value, grp, size = 0, offset = 0.01,
            col.ends.dens = "#A1D99B", col.mid.dens = "#E5F5E0")

# A power transformation can be applied to the data. Here
# we'll apply a log transformation
eda_normfit(dat, value, grp, p = 0)

mgimond/tukeyedar documentation built on March 19, 2024, 8:44 a.m.