set_zeta_phylo: Set zeta hyperparameter for coalescent data
In jrfaulkner/spmrf: Shrinkage-Prior Markov Random Field Smoothing

Description Usage Arguments Details Value References See Also

Calculate a value for the hyperparameter on the global smoothing prior for coalescent data

1	set_zeta_phylo(phylo, ncell, upBound = NULL, alpha = 0.05, order = 1)

`phylo`	A list containing a numeric vector of coalescent times (`coal_times`), a numeric vector of sampling times (`samp_times`, and a integer vector of number of samples taken at each sampling time (`n_sampled`)
`ncell`	The number of grid cells from the uniformly spaced grid over which effective population size is to be estimated.
`upBound`	Upper bound on the expected value of the marginal standard deviations of the log latent trend (field) parameters. This value is rarely known a priori and here is assumed to equal the standard deviation of the observed data (skyline estimates in this case) unless otherwise specified.
`alpha`	The probability of exceeding `upBound`.
`order`	The order of the SPMRF model (1 or 2).

This function can be used to calculate reasonable values for the hyperparameter zeta, which controls the scale (and median) of the half-Cauchy prior on the global smoothing parameter for the latent field of trend parameters (on the log scale) of an spmrf model applied to coalescent data.

Making alpha smaller will decrease the size of zeta, which will result in smoother latent trends if the information in the data does not overcome the prior information.

The methods for calculation of the hyperparameter zeta are outlined in Faulkner and Minin (2018) and are based on methods introduced by Sorbye and Rue (2014) for setting hyperparameters for the precision of Gaussian Markov random field priors.

A numeric scalar value for the hyperparmeter zeta, where zeta > 0.

Faulkner, J. R., and V. N. Minin. 2018. Locally adaptive smoothing with Markov random fields and shrinkage priors. Bayesian Analysis 13(1):225-252.

Sorbye, S. and H. Rue. 2014. Scaling intrinsic Gaussian Markov random field priors in spatial modelling. Spatial Statistics 8:39-51.

spmrf, set_zeta

jrfaulkner/spmrf documentation built on Sept. 27, 2020, 12:34 p.m.