like.resamp: Local Optimums and Covariance from the optimizer step.

In strandCet: Estimation of Biological Parameters from Stranded Cetaceans

Description

Defines some necessary arguments for the function final.resamp. Removes NAs from the opt.mu.d and opt.cov.d matrixes.

Usage

like.resamp(K, log.like.0, opt.cov.d, opt.mu.d, d.keep, d = 10,
  theta.dim = 9)

Arguments

K	Number of iterations at the importance sampling stage.
log.like.0	A vector containing the likelihoods for each row of the prior.
opt.cov.d	Covariance matrixes for the local optimums.
opt.mu.d	A d x 8 matrix containing the local optimums (sets of parameters from the optimizer step).
d.keep	Number of local optimums found in the optimizer step.
d	A scalar defining the number of optimizer interations.
theta.dim	Number of columns in the prior matrix.

Value

h.mu	A d.keep * 8 matrix containing the local optimum result.
h.sig	An array with (theta.dim * theta.dim * (K + d.keep)) dimensions containing the covariance matrix for each local optimum.
log.like	A vector of likelihoods for each row of H.k.

Note

Typically for use immediately before running final.resamp or within the function HP.mod

References

Poole, D. and Raftery, A. (2000). Inference for Deterministic Simulation Models: The Bayesian Melding Approach. Journal of the American Statistical Association 95:1244<e2><80><93>1255.

Raftery, A. and Bao, L. (2009). Estimating and Projecting Trends in HIV/AIDS Generalized Epidemics Using Incremental Mixture Importance Sampling. Technical Report 560, Department of Statistics, University of Washington.

Sharrow, D.J., Clark, S.J., Collinson, M.A., Kahn, K. and Tollman, S.M. (2013). The Age Pattern of Increases in Mortality Affected by HIV: Bayesian Fit of the Heligman-Pollard Model to Data from the Agincourt HDSS Field Site in Rural Northeast South Africa. Demogr. Res. 29, 1039<e2><80><93>1096.

See Also

final.resamp HP.mod

strandCet documentation built on May 1, 2019, 8:19 p.m.