Compute colors in classes distribution

colors.in.classes(
  classes,
  color1,
  color2 = NULL,
  mask = array(TRUE, dim(classes)),
  N = max(classes, na.rm = TRUE),
  type = "tresh",
  thresh1 = NULL,
  thresh2 = NULL,
  sd1 = 2,
  sd2 = 2,
  col1 = "green",
  col2 = "red",
  test = FALSE,
  plot = TRUE,
  beside = TRUE,
  ylim = NULL,
  verbose = FALSE,
  ...
)

Arguments

classes: Image of classes
color1: Image of first color
color2: Image of second color
mask: Image mask
N: Maximum number of classes
type: Type of spot definition, see details
thresh1: Threshold for first color image
thresh2: Threshold for second color image
sd1: For automatic threshold, that is: mean(color1)+sd1*sd(color1)
sd2: For automatic threshold of color2
col1: Name of color 1
col2: Name of color 2
test: Compute tests: "Wilcoxon" for Wilcoxon rank-sum (Mann-Whitney U), chisq for Chi-squared test
plot: Plot barplots
beside: a logical value. If FALSE, the columns of height are portrayed as stacked bars, and if TRUE the columns are portrayed as juxtaposed bars.
ylim: limits for the y axis (plot)
verbose: verbose mode
...: additional plotting parameters

Value

Table of classes with color 1 (and 2)

Details

Type of spot definitions: "thresh" or "t": Threshold based (threshold can be given by thresh1/2 or automatically derived) "voxel" or "v": Spots are given as binary voxel mask "intensity" or "i": Voxels are weighted with voxel intensity. Intensity is scaled to [0,1] after subtracting thresh1/2 (or automatic threshold)