wandafromx: Find weight and inverse scale parameter from data if Laplace...
In stephenslab/EbayesThresh: Empirical Bayes Thresholding and Related Methods

Description Usage Arguments Details Value Author(s) References See Also Examples

Given a vector of data and a single value or vector of sampling standard deviations, find the marginal maximum likelihood choice of both weight and inverse scale parameter under the Laplace prior.

1 2	wandafromx(x, s = 1, universalthresh = TRUE) negloglik.laplace(xpar, xx, ss, tlo, thi)

`x`	A vector of data.
`s`	A single value or a vector of standard deviations. If vector, must have the same length as `x`.
`universalthresh`	If `universalthresh = TRUE`, the thresholds will be upper bounded by universal threshold; otherwise, the thresholds can take any non-negative values.
`xx`	A vector of data.
`xpar`	Vector of two parameters: `xpar[1]` : a value between 0 and 1, which will be adjusted to range of w; `xpar[2]`, inverse scale (i.e., rate) parameter `a`.
`ss`	Vector of standard deviations.
`tlo`	Lower bound of thresholds.
`thi`	Upper bound of thresholds.

The parameters are found by marginal maximum likelihood.

The search is over weights corresponding to threshold t_i in the range [0, s_i sqrt(2 log n)] if universalthresh=TRUE, where n is the length of the data vector and (s_1, ... , s_n) is the vector of sampling standard deviation of data (x_1, ... , x_n); otherwise, the search is over [0,1].

The search uses a nonlinear optimization routine (optim in R) to minimize the negative log likelihood function negloglik.laplace. The range over which the inverse scale parameter is searched is (0.04, 3). For reasons of numerical stability within the optimization, the prior is parametrized internally by the threshold and the inverse scale parameter.

A list with values: