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 |
positive 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)