wmonfromx {EbayesThresh} | R Documentation |
Given a vector of data, find the marginal maximum likelihood choice of weight sequence subject to the constraints that the weights are monotone decreasing.
wmonfromx(xd, prior = "laplace", a = 0.5, tol = 1e-08, maxits = 20)
xd |
a vector of data |
prior |
specification of the prior to be used; can be cauchy or
laplace |
a |
scale parameter in prior if prior="laplace" . Ignored if prior="cauchy" |
tol |
absolute tolerance to within which estimates are calculated |
maxits |
maximum number of weighted least squares iterations within the calculation |
The weights is found by marginal maximum likelihood. The search is over weights corresponding to thresholds in the range [0, sqrt{2 log n}], where n is the length of the data vector.
An iterated least squares monotone regression algorithm
is used to maximize the log likelihood.
The weighted least squares monotone regression routine
isotone
is used.
To turn the weights into thresholds, use the routine tfromw
;
to process the data with these thresholds,
use the routine threshld
.
The vector of estimated weights is returned
Bernard Silverman
See ebayesthresh
and http://www.bernardsilverman.com