isotone: Weighted least squares monotone regression

In stephenslab/EbayesThresh: Empirical Bayes Thresholding and Related Methods

Description

Given a vector of data and a vector of weights, find the monotone sequence closest to the data in the sense of weighted least squares with the given weights.

Usage

isotone(x, wt = rep(1, length(x)), increasing = FALSE)

Arguments

x	a vector of data
wt	a vector the same length as x, giving the weights to be used in the weighted least squares algorithm
increasing	logical variable indicating whether the required fit is to be increasing or decreasing

Details

The standard pool-adjacent-violators algorithm is used. Maximal decreasing subsequences are found within the current sequence. Each such decreasing subsequence is replaced by a constant sequence with value equal to the weighted average. Within the algorithm, the subsequence is replaced by a single point, with weight the sum of the weights within the subsequence. This process is iterated to termination. The resulting sequence is then unpacked back to the original ordering to give the weighted least squares monotone fit.

If increasing = FALSE, the original sequence is negated and the resulting estimate negated.

Value

The vector giving the best fitting monotone sequence is returned.

Author(s)

Bernard Silverman

References

See ebayesthresh and http://www.bernardsilverman.com

See Also

wmonfromx

stephenslab/EbayesThresh documentation built on May 15, 2019, 4:28 p.m.