pracma (version 1.9.3)

cutpoints: Find Cutting Points

Description

Finds cutting points for vector s of real numbers.

Usage

cutpoints(x, nmax = 8, quant = 0.95)

Arguments

x

vector of real values.

nmax

the maximum number of cutting points to choose

quant

quantile of the gaps to consider for cuts.

Value

Returns a list with components cutp, the cutting points selected, and cutd, the gap between values of x at this cutting point.

Details

Finds cutting points for vector s of real numbers, based on the gaps in the values of the vector. The number of cutting points is derived from a quantile of gaps in the values. The user can set a lower limit for this number of gaps.

References

Witten, I. H., and E. Frank (2005). Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann Publishers, San Francisco.

See Also

cut

Examples

N <- 100; x <- sort(runif(N))
cp <- cutpoints(x, 6, 0.9)
n <- length(cp$cutp)

# Print out
nocp <- rle(findInterval(x, c(-Inf, cp$cutp, Inf)))$lengths
cbind(c(-Inf, cp$cutp), c(cp$cutp, Inf), nocp)

# Define a factor from the cutting points
fx <- cut(x, breaks = c(-Inf, cp$cutp, Inf))

## Not run: 
# # Plot points and cutting points
# plot(x, rep(0, N), col="gray", ann = FALSE)
# points(cp$cutp, rep(0, n), pch="|", col=2)
# 
# # Compare with k-means clustering
# km <- kmeans(x, n)
# points(x, rep(0, N), col = km$cluster, pch = "+")
# 
# ##  A 2-dimensional example
# x <- y <- c()
# for (i in 1:9) {
#   for (j in 1:9) {
#     x <- c(x, i + rnorm(20, 0, 0.2))
#     y <- c(y, j + rnorm(20, 0, 0.2))
#   }
# }
# cpx <- cutpoints(x, 8, 0)
# cpy <- cutpoints(y, 8, 0)
# 
# plot(x, y, pch = 18, col=rgb(0.5,0.5,0.5), axes=FALSE, ann=FALSE)
# for (xi in cpx$cutp) abline(v=xi, col=2, lty=2)
# for (yi in cpy$cutp) abline(h=yi, col=2, lty=2)
# 
# km <- kmeans(cbind(x, y), 81)
# points(x, y, col=km$cluster)
# ## End(Not run)