categorize {cases} | R Documentation |
Categorize continuous values
Description
This function allows to split continuous values, e.g. (risk) scores or (bio)markers, into two or more categories by specifying one or more cutoff values.
Usage
categorize(
values,
cutoffs = rep(0, ncol(values)),
map = 1:ncol(values),
labels = NULL
)
Arguments
values |
numeric matrix of continuous values to be categorized. Assume an (n x r) matrix with n observations (subjects) of r continuous values. |
cutoffs |
numeric matrix of dimension m x k. Each row of cutoffs defines a split into k+1 distinct categories. Each row must contain distinct values. In the simplest case, cutoffs is a single column matrix whereby is row defines a binary split (<=t vs. >t). In this case (k=1), cutoffs can also be a numeric vector. |
map |
integer vector of length k with values in 1:r, whereby r = ncol(values). map_l gives the value which column of values should be categorized by ... |
labels |
character of length m (= number of prediction r) |
Value
numeric (n x k) matrix with categorical outcomes after categorizing.
Examples
set.seed(123)
M <- as.data.frame(mvtnorm::rmvnorm(20, mean=rep(0, 3), sigma=2*diag(3)))
M
categorize(M)
C <- matrix(rep(c(-1, 0, 1, -2, 0, 2), 3), ncol=3, byrow = TRUE)
C
w <- c(1, 1, 2, 2, 3, 3)
categorize(M, C, w)