QQ plot matrix — eda_qqmat • tukeyedar

eda_qqmat Generates a matrix of empirical QQ plots

Usage

eda_qqmat(
  dat,
  x,
  fac,
  p = 1L,
  tukey = FALSE,
  q.type = 5,
  upper = FALSE,
  xylim = NULL,
  resid = FALSE,
  stat = mean,
  plot = TRUE,
  grey = 0.6,
  pch = 21,
  p.col = "grey40",
  p.fill = "grey60",
  size = 1,
  text.size = 1,
  tail.pch = 21,
  tail.p.col = "grey70",
  tail.p.fill = NULL,
  tic.size = 0.7,
  alpha = 0.8,
  q = FALSE,
  tails = FALSE,
  med = FALSE,
  inner = 0.75,
  ...
)

Arguments

dat: Data frame.
x: Continuous variable.
fac: Categorical variable.
p: Power transformation to apply to the continuous variable.
tukey: Boolean determining if a Tukey transformation should be adopted (FALSE adopts a Box-Cox transformation).
q.type: An integer between 1 and 9 selecting one of the nine quantile algorithms. (See quantiletile function).
upper: Boolean determining if both upper and lower triangular matrix should be plotted. If set to FALSE, only the lower triangular matrix is plotted.
xylim: X and Y axes limits.
resid: Boolean determining if residuals should be plotted. Residuals are computed using the stat parameter.
stat: Statistic to use if residuals are to be computed. Currently mean (default) or median.
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 symbol size (0-1).
text.size: Size for category text in diagonal box.
tail.pch: Tail-end point symbol type (See tails).
tail.p.col: Tail-end color for point symbol (See tails).
tail.p.fill: Tail-end point fill color passed to bg (Only used for tail.pch ranging from 21-25).
tic.size: Size of tic labels (defaults to 0.8).
alpha: Point transparency (0 = transparent, 1 = opaque). Only applicable if rgb() is not used to define point colors.
q: Boolean determining if grey box highlighting the inner region should be displayed.
tails: Boolean determining if points outside of the inner region should be symbolized differently. Tail-end points are symbolized via the tail.pch, tail.p.col and tail.p.fill arguments.
med: Boolean determining if median lines should be drawn.
inner: Fraction of mid-values to highlight in q or tails. Defaults to the inner 75 percent of values.
...: Not used

Value

Returns a list with the following components:

data: List with input x and y values for each group. May be interpolated to smallest quantile batch if batch sizes don't match. Values will reflect power transformation defined in p.
p: Transformation applied to original values.

Details

The function will generate an empirical QQ plot matrix. Most of the arguments available in eda_qq are echoed in this function. The one notable difference is the default settings. By default, eda_qqmat will generate a plain vanilla set of plots.

The QQ plot matrix is most effective in comparing residuals after the data are fitted by the mean or median. To plot the residuals, set resid=TRUE. By default, the mean is used. You can change the statistic to the median by setting stat=median.

The function also allows for batch transformation of values via the p argument. The transformation is applied to the data prior to computing the residuals.

References

John M. Chambers, William S. Cleveland, Beat Kleiner, Paul A. Tukey. Graphical Methods for Data Analysis (1983)
Quantile-Quantile plot article

Examples


# Default output
singer <- lattice::singer
eda_qqmat(singer, height, voice.part)


# Symbolize points outside of the "inner" region using an open point symbol
eda_qqmat(singer, height, voice.part, tails = TRUE)


# Set the inner region to cover 80% and change the outer point symbol to "+"
eda_qqmat(singer, height, voice.part, inner = 0.8, tails = TRUE, tail.pch = 3)


# Add the upper triangle to the matrix
eda_qqmat(singer, height, voice.part, upper = TRUE)


# Plot residuals after fitting mean to each batch
eda_qqmat(singer, height, voice.part, resid = TRUE)


# Log transform the data, then plot the residuals after fitting the mean model
eda_qqmat(iris, Petal.Length, Species, resid = TRUE, p = 0)
#> Note that a power transformation of 0 was applied to the data before they were processed for the plot.


# Fit the median model instead of the mean
eda_qqmat(iris, Petal.Length, Species, resid = TRUE, p = 0, stat = median)
#> Note that a power transformation of 0 was applied to the data before they were processed for the plot.


# Shade the "inner" regions (defaults to the mid 70% of values)
eda_qqmat(iris, Petal.Length, Species, resid = TRUE, q = TRUE, p = 0)
#> Note that a power transformation of 0 was applied to the data before they were processed for the plot.


# Change inner region point symbols to dark orange and change inner region
# range to cover 90% of mid values
eda_qqmat(iris, Petal.Length, Species, resid = TRUE, p = 0, inner = 0.9,
          tail.pch = 3, p.fill = "orange2")
#> Note that a power transformation of 0 was applied to the data before they were processed for the plot.