R: Find monotone Empirical Bayes weights from data.

wmonfromx {EbayesThresh} | R Documentation

## Find monotone Empirical Bayes weights from data.

### Description

Given a vector of data, find the marginal maximum likelihood choice of weight sequence subject to the constraints that the weights are monotone decreasing.

### Usage

    wmonfromx(xd, prior = "laplace", a = 0.5, tol = 1e-08, maxits = 20)

### Arguments

`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.

### Details

The weights is found by marginal maximum likelihood. The search is over weights corresponding to thresholds in the range [0, √{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`.

### Value

The vector of estimated weights is returned.

### Author(s)

Bernard Silverman

### References

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

### See Also

`wfromx`, `isotone`

[Package EbayesThresh version 1.4-12 Index]