SAMCrsa: A Resampling-based Stochastic Approximation Method for...
In SAMCpack: Stochastic Approximation Monte Carlo (SAMC) Sampler and Methods

Description Usage Arguments Value Author(s) References Examples

Performs parameter estimation using a resampling-based Stochastic Approximation (RSA) method. It is a stochatic approximation method. At every iteration, only a subset of the data is drawn and used to update the estimation of the parameters. The data are assumed to have a powered exponential correlation structure.

1 2	SAMCrsa(coords, y, X = NULL, nsubset = max(ceiling(length(y)/5), 10), stepscale = 200, niter = 2500, warm = 100)

`coords`	an (n\times 2) matrix. 2D location coordinates.
`y`	a length-n vector of response value.
`X`	an (n\times k) matrix of extra covariates.
`nsubset`	the size of the subset drawn from the data. It is recommended to be set to 300 or higher.
`stepscale`	gain factor control. It specifies the number of iterations when the gain factor begins to shrink. For example, one can be set it equal to 2 times the burn-in steps.
`niter`	the total number of iterations for stochastic approximation. In practice, it is recommended to be set to 2500 or higher.
`warm`	the number of burn-in iterations

a named list containing

beta: the coefficient estimates of the mean effect. It is a vector of length equal to the number of coefficients plus 1.
phi: the shape estimate in the powered exponential correlation matrix.
sigmasq: the estimate of error variance.
tausq: the estimate of nugget variance.

Yichen Cheng, Faming Liang, Kisung You

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SAMCrsaSAMCpack

##### load example data pre-loaded
data(gdata)

##### run RSA
output = SAMCrsa(gdata$coords, gdata$y, gdata$X, nsubset=50, stepscale=40, niter=100, warm=20)