S4 classes around infrastructure provided by the dclone package to make package development with data cloning for hierarchical models easy as a breeze.

The package defines S4 object classes for plain BUGS models
(`"gsFit"`

, after BU*GS*/JA*GS*),
and BUGS models made ready for data cloning
(`"dcFit"`

).
It also defines virtual classes for S3 object classes defined in
the dclone and coda packages.

The S4 class `"dcmle"`

is a fitted
model object containing MCMC results as returned by the
`dcmle`

function.
These object classes are easily
extensible to allow inclusion into functions fitting
specific models to the data (see Examples).

Peter Solymos

Maintainer: Peter Solymos <solymos@ualberta.ca>

Forum: https://groups.google.com/forum/#!forum/dclone-users

Issues: https://github.com/datacloning/dcmle/issues

Data cloning website: http://datacloning.org

Fitting wrapper function: `dcmle`

Object classes: `"dcmle"`

,
`"codaMCMC"`

, `"dcCodaMCMC"`

Creator functions `makeGsFit`

and
`makeDcFit`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 | ```
## Data and model taken from Ponciano et al. 2009
## Ecology 90, 356-362.
## Function to create template object for the Beverton-Holt model
## R CMD check will not choke on character representation of model
## the convenient makeDcFit creator function is used here
bevholtFit <-
function(y) {
makeDcFit(
data = list(ncl=1, n=length(y), Y=dcdim(data.matrix(y))),
model = structure(
c("model {",
" for (k in 1:ncl) {",
" for(i in 2:(n+1)) {",
" Y[(i-1), k] ~ dpois(exp(X[i, k]))",
" X[i, k] ~ dnorm(mu[i, k], 1 / sigma^2)",
" mu[i,k] <- X[(i-1),k]+log(lambda)-log(1+beta*exp(X[(i-1),k]))",
" }",
" X[1, k] ~ dnorm(mu0, 1 / sigma^2)",
" }",
" beta ~ dlnorm(-1, 1)",
" sigma ~ dlnorm(0, 1)",
" tmp ~ dlnorm(0, 1)",
" lambda <- tmp + 1",
" mu0 <- log(2) + log(lambda) - log(1 + beta * 2)",
"}"),
class = "custommodel"),
multiply = "ncl",
unchanged = "n",
params <- c("lambda","beta","sigma"))
}
## S4 class 'bevholtMle' extends the 'dcmle' class
## it can have additional slots
setClass("bevholtMle",
representation(y="numeric", title="character"),
contains = "dcmle")
## Function to fit the Beverton-Holt model to data
bevholt <- function(y, n.clones, ...) {
new("bevholtMle",
dcmle(bevholtFit(y), n.clones=n.clones, ...),
y = y,
title = "Beverton-Holt Model")
}
## Show method with appropriate heading
setMethod("show", "bevholtMle", function(object)
show(summary(as(object, "dcmle"), object@title)))
paurelia <- c(17,29,39,63,185,258,267,392,510,
570,650,560,575,650,550,480,520,500)
## Not run:
(m <- bevholt(paurelia, n.clones=2, n.iter=1000))
vcov(m)
m@y
## End(Not run)
``` |

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