| param {ramps} | R Documentation |
Function used in conjunction with ramps.control to specify the initial values and prior distributions used in calls to georamps.
param(init, prior = c("flat", "invgamma", "normal", "uniform"), tuning = 1,
...)
init |
numerical vector of initial parameter values. NA elements will be replaced with random draws from the prior distribution when possible. |
prior |
character string specifying the prior distribution. This must be one of "flat", "invgamma", "normal", or "uniform", with default "flat", and may be abbreviated to a unique prefix. |
tuning |
numerical values for tuning the MCMC sampler. |
... |
hyperparameters of the specified prior distribution. See details below. |
The supported prior distributions and associated hyperparameters are:
"flat""invgamma"shape > 0 and scale > 0 such that f(x) = scale^shape / gamma(shape) * x^{-shape - 1} * exp(-scale / x)."normal"mean and variance such that (2*pi)^(-n/2) * det(variance)^(-1/2) * exp(-1/2 * t(x - mean) %*% solve(variance) %*% (x - mean)). The variance hyperparameter must be positive definite and may be supplied either as a vector (independence) or a matrix."uniform"min and max > min such that f(x) = 1 / (max - min).
The number of model parameters to be initialized is determined by length(init). Missing values occurring in the supplied init vector will be replaced with draws from the prior distribution, for all but the "flat" specification.
A list of class 'param' containing the following components:
init |
numerical vector of initial parameter values. |
prior |
character string specifying the prior distribution. |
tuning |
numerical vector of tuning values of length(init). |
... |
hyperparameters of the specified prior distribution. |
Brian Smith brian-j-smith@uiowa.edu
## Initial values for a flat prior param(rep(0, 2), "flat") ## Random generation of initial values for an inverse-gamma prior param(rep(NA, 2), "invgamma", shape = 2.0, scale = 0.1) ## Independent normal priors param(rep(0, 2), "normal", mean = c(0, 0), variance = c(100, 100)) ## Correlated normal priors npv <- rbind(c(100, 25), c(25, 100)) param(rep(0, 2), "normal", mean = c(0, 0), variance = npv) ## Uniform prior and MCMC tuning parameter specification param(10, "uniform", min = 0, max = 100, tuning = 0.5)