param | 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", "user"), tuning, ...)
init |
numerical vector of initial parameter values. |
prior |
character string specifying the prior distribution. This must be one of |
tuning |
numerical tuning values the slice-simplex routine in the MCMC sampler. |
... |
hyperparameters of the specified prior distribution. See details below. |
The supported prior distributions and associated hyperparameters are:
"flat"
Flat prior with no hyperparameters.
"invgamma"
Inverse-gamma with hyperparameters shape > 0
and scale > 0
such that f(x) = scale^shape / gamma(shape) * x^{-shape - 1} * exp(-scale / x).
"normal"
Normal with hyperparameters 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"
Uniform with hyperparameters min
and max > min
such that f(x) = 1 / (max - min).
"user"
Use-defined function supplied as hyperparameter f
which takes a single numeric vector of length and order equal to the associated model parameters and whose returns values are proportional to the prior distribution.
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 |
... |
hyperparameters of the specified prior distribution. |
Brian Smith brian-j-smith@uiowa.edu
georamps
,
ramps.control
## 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)
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