Tukey's spread-level function — eda

The eda_sl function generates a spread-level table from a univariate dataset.

Usage

eda_sl(
  dat,
  x,
  fac,
  p = 1,
  tukey = FALSE,
  sprd = "frth",
  plot = TRUE,
  grey = 0.6,
  pch = 21,
  p.col = "grey50",
  p.fill = "grey80",
  size = 1,
  alpha = 0.8
)

Arguments

dat: Dataframe
x: Continuous variable column
fac: Categorical variable column
p: Power transformation to apply to variable
tukey: Boolean determining if a Tukey transformation should be adopted (FALSE adopts a Box-Cox transformation)
sprd: Choice of spreads. Either interquartile, sprd = "IQR" or fourth-spread, sprd = "frth" (default).
plot: Boolean determining if plot should be generated.
grey: Grey level to apply to plot elements (0 to 1 with 1 = black).
pch: Point symbol type.
p.col: Color for point symbol.
p.fill: Point fill color passed to bg (Only used for pch ranging from 21-25).
size: Point size (0-1)
alpha: Point transparency (0 = transparent, 1 = opaque). Only applicable if rgb() is not used to define point colors.

Value

Returns a dataframe of level and spreads.

Details

Note that this function is not to be confused with William Cleveland's spread-location function.
If fac is not categorical, the output will produce many or all NA's.
On page 59, Hoaglan et. al define the fourth-spread as the the range defined by the upper fourth and lower fourth. The eda_lsum function is used to compute the upper/lower fourths.

References

Understanding Robust and Exploratory Data Analysis, Hoaglin, David C., Frederick Mosteller, and John W. Tukey, 1983.

Examples

dat <- read.csv("http://mgimond.github.io/ES218/Data/Food_web.csv")
sl <- eda_sl(dat, mean.length, dimension)


# The output can be passed to a model fitting function like eda_lm
# The output slope can be used to help identify a power transformation
eda_lm(sl, Level, Spread)

#> (Intercept)           x 
#>   -2.969986    2.979117