noteworthy: Find noteworthy (unusual) points in a 2D plot

In heplots: Visualizing Hypothesis Tests in Multivariate Linear Models

Find noteworthy (unusual) points in a 2D plot

Description

This function extends the logic used by showLabels to provide a more general collection of methods to identify unusual or "noteworthy" points in a two-dimensional display. Standard methods include Mahalanobis and Euclidean distance from the centroid, absolute value of distance from the mean of X or Y, absolute value of Y and absolute value of the residual in a model Y ~ X.

Usage

noteworthy(x, y = NULL, n = length(x), method = "mahal", level = NULL, ...)

Arguments

x, y	The x and y coordinates of a set of points. Alternatively, a single argument x can be provided, since xy.coords(x, y) is used for construction of the coordinates.
n	Maximum number of points to identify. If set to 0, no points are identified.
method	Method of point identification. See Details.
level	Where appropriate, if supplied, the identified points are filtered so that only those for which the criterion is < level
...	Other arguments, silently ignored

Details

The 'method' argument determines how the points to be identified are selected:

"mahal"

Treat (x, y) as if it were a bivariate sample, and select cases according to their Mahalanobis distance from (mean(x), mean(y)).

"dsq"

Similar to "mahal" but uses squared Euclidean distance.

"x"

Select points according to their value of abs(x - mean(x)).

"y"

Select points according to their value of abs(y - mean(y)).

"r"

Select points according to their value of abs(y), as may be appropriate in residual plots, or others with a meaningful origin at 0, such as a chi-square QQ plot.

"ry"

Fit the linear model, y ~ x and select points according to their absolute residuals.

case IDs

method can be an integer vector of case numbers in 1:length{x}, in which case those cases will be labeled.

numeric vector

method can be a vector of the same length as x consisting of values to determine the points to be labeled. For example, for a linear model mod, setting method=cooks.distance(mod) will label the n points corresponding to the largest values of Cook's distance. Warning: If missing data are present, points may be incorrectly selected.

In the case of method == "mahal" a value for level can be supplied. This is used as a filter to select cases whose criterion value exceeds level. In this case, the number of points identified will be less than or equal to n.

Examples

# example code
set.seed(47)
x <- c(runif(100), 1.5, 1.6, 0)
y <- c(2*x[1:100] + rnorm(100, sd = 1.2), -2, 6, 6 )
z <- y - x
mod <- lm(y ~ x)

# testing function to compare noteworthy with car::showLabels() 
testnote <- function(x, y, n, method=NULL, ...)  {
  plot(x, y)
  abline(lm(y ~ x))
  if (!is.null(method))
    car::showLabels(x, y, n=n, method = method) |> print()
  ids <- noteworthy(x, y, n=n, method = method, ...)
  text(x[ids], y[ids], labels = ids, col = "red")
  ids
  }

  # Mahalanobis distance 
testnote(x, y, n = 5, method = "mahal")
testnote(x, y, n = 5, method = "mahal", level = .99)
  # Euclidean distance 
testnote(x, y, n = 5, method = "dsq")

testnote(x, y, n = 5, method = "y")
testnote(x, y, n = 5, method = "ry")

  # a vector of criterion values
testnote(x, y, n = 5, method = Mahalanobis(data.frame(x,y)))
testnote(x, y, n = 5, method = z)
  # vector of case IDs
testnote(x, y, n = 4, method = seq(10, 60, 10))
testnote(x, y, n = 4, method = which(cooks.distance(mod) > .25))

  # test use of xy.coords  
noteworthy(data.frame(x,y), n=4)
noteworthy(y ~ x, n=4)

heplots documentation built on April 17, 2025, 1:08 a.m.