Package 'dcmle' reference manual

Title:	Hierarchical Models Made Easy with Data Cloning
Description:	S4 classes around infrastructure provided by the 'coda' and 'dclone' packages to make package development easy as a breeze with data cloning for hierarchical models.
Authors:	Peter Solymos [cre, aut]
Maintainer:	Peter Solymos <[email protected]>
License:	GPL-2
Version:	0.4-1
Built:	2024-08-19 05:18:17 UTC
Source:	https://github.com/datacloning/dcmle

Hierarchical Models Made Easy with Data Cloning

Description

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

Details

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).

Author(s)

Peter Solymos

Maintainer: Peter Solymos <[email protected]>

References

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

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

Data cloning website: https://datacloning.org

Examples

## 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)
## 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)

coda package related generic functions

Description

coda package related generic functions.

Usage

chanames(x, ...)
varnames(x, ...)
chanames(x, ...)
varnames(x, ...)

Arguments

`x`	MCMC object.
`...`	Other arguments.

Value

See corresponding help pages.

Author(s)

Peter Solymos

Class `"codaMCMC"`

Description

An S4 representation of an mcmc.lits object of the coda package.

Objects from the Class

Objects can be created by calls of the form new("codaMCMC", ...).

Slots

values:: Object of class "numeric", values from the posterior sample of length niter * nvar * nchains.
varnames:: Object of class "character", variable names.
start:: Object of class "integer", start of iterations.
end:: Object of class "integer", end of iterations.
thin:: Object of class "integer", thinning value.
nchains:: Object of class "integer", number of chains.
niter:: Object of class "integer", number of iterations.
nvar:: Object of class "integer", number of variables

Methods

[: signature(x = "codaMCMC"): ...
[[: signature(x = "codaMCMC"): ...
acfplot: signature(x = "codaMCMC"): ...
as.array: signature(x = "codaMCMC"): ...
as.matrix: signature(x = "codaMCMC"): ...
as.mcmc.list: signature(x = "codaMCMC"): ...
autocorr.diag: signature(mcmc.obj = "codaMCMC"): ...
chanames: signature(x = "codaMCMC"): ...
chisq.diag: signature(x = "codaMCMC"): ...
coef: signature(object = "codaMCMC"): ...
coerce: signature(from = "codaMCMC", to = "dcmle"): ...
coerce: signature(from = "codaMCMC", to = "MCMClist"): ...
coerce: signature(from = "dcmle", to = "codaMCMC"): ...
coerce: signature(from = "MCMClist", to = "codaMCMC"): ...
confint: signature(object = "codaMCMC"): ...
crosscorr.plot: signature(x = "codaMCMC"): ...
crosscorr: signature(x = "codaMCMC"): ...
cumuplot: signature(x = "codaMCMC"): ...
dcdiag: signature(x = "codaMCMC"): ...
dcsd: signature(object = "codaMCMC"): ...
dctable: signature(x = "codaMCMC"): ...
densityplot: signature(x = "codaMCMC"): ...
densplot: signature(x = "codaMCMC"): ...
end: signature(x = "codaMCMC"): ...
frequency: signature(x = "codaMCMC"): ...
gelman.diag: signature(x = "codaMCMC"): ...
gelman.plot: signature(x = "codaMCMC"): ...
geweke.diag: signature(x = "codaMCMC"): ...
head: signature(x = "codaMCMC"): ...
heidel.diag: signature(x = "codaMCMC"): ...
lambdamax.diag: signature(x = "codaMCMC"): ...
mcpar: signature(x = "codaMCMC"): ...
nchain: signature(x = "codaMCMC"): ...
nclones: signature(x = "codaMCMC"): ...
niter: signature(x = "codaMCMC"): ...
nvar: signature(x = "codaMCMC"): ...
pairs: signature(x = "codaMCMC"): ...
plot: signature(x = "codaMCMC", y = "missing"): ...
qqmath: signature(x = "codaMCMC"): ...
quantile: signature(x = "codaMCMC"): ...
raftery.diag: signature(x = "codaMCMC"): ...
show: signature(object = "codaMCMC"): ...
stack: signature(x = "codaMCMC"): ...
start: signature(x = "codaMCMC"): ...
summary: signature(object = "codaMCMC"): ...
tail: signature(x = "codaMCMC"): ...
thin: signature(x = "codaMCMC"): ...
time: signature(x = "codaMCMC"): ...
traceplot: signature(x = "codaMCMC"): ...
varnames: signature(x = "codaMCMC"): ...
vcov: signature(object = "codaMCMC"): ...
window: signature(x = "codaMCMC"): ...
xyplot: signature(x = "codaMCMC"): ...

Author(s)

Peter Solymos

Examples

showClass("codaMCMC")
showClass("codaMCMC")

Generic after similar coda function

Description

Generic after similar coda function

Usage

crosscorr.plot(x, ...)
crosscorr.plot(x, ...)

Arguments

`x`	MCMC object.
`...`	Other arguments.

Value

See corresponding help page

Author(s)

Peter Solymos

Generic after similar coda function

Description

Generic after similar coda function

Usage

cumuplot(x, ...)
cumuplot(x, ...)

Arguments

`x`	MCMC object.
`...`	Other arguments.

Value

See corresponding help page

Author(s)

Peter Solymos

Class `"custommodel"`

Description

Stands for the 'custommodel' S3 class from dclone package.

Objects from the Class

A virtual Class: No objects may be created from it.

Extends

Class "dcModel", directly.

Methods

No methods defined with class "custommodel" in the signature.

Author(s)

Peter Solymos

Examples

showClass("custommodel")
showClass("custommodel")

Class `"dcArgs"`

Description

A class union for NULL and "character".

Objects from the Class

A virtual Class: No objects may be created from it.

Methods

No methods defined with class "dcArgs" in the signature.

Author(s)

Peter Solymos

Examples

showClass("dcArgs")
showClass("dcArgs")

Class `"dcCodaMCMC"`

Description

An S4 representation of an mcmc.list object of the coda package, with data cloning attributes from dclone package (the mcmc.list.dc class).

Objects from the Class

Objects can be created by calls of the form new("dcCodaMCMC", ...).

Slots

dctable:: Object of class "dcTable", data cloning based iterative posterior statistics based on dctable.
dcdiag:: Object of class "dcDiag", data cloning convergence diagnostics based on dcdiag.
nclones:: Object of class "nClones", number of clones.
values:: Object of class "numeric", same as in "codaMCMC" class.
varnames:: Object of class "character", same as in "codaMCMC" class.
start:: Object of class "integer", same as in "codaMCMC" class.
end:: Object of class "integer", same as in "codaMCMC" class.
thin:: Object of class "integer", same as in "codaMCMC" class.
nchains:: Object of class "integer", same as in "codaMCMC" class.
niter:: Object of class "integer", same as in "codaMCMC" class.
nvar:: Object of class "integer", same as in "codaMCMC" class.

Extends

Class "codaMCMC", directly.

Methods

[: signature(x = "dcCodaMCMC"): ...
[[: signature(x = "dcCodaMCMC"): ...
coerce: signature(from = "dcCodaMCMC", to = "dcmle"): ...
coerce: signature(from = "dcCodaMCMC", to = "MCMClist"): ...
coerce: signature(from = "dcmle", to = "dcCodaMCMC"): ...
coerce: signature(from = "MCMClist", to = "dcCodaMCMC"): ...
confint: signature(object = "dcCodaMCMC"): ...
dcdiag: signature(x = "dcCodaMCMC"): ...
dctable: signature(x = "dcCodaMCMC"): ...
nclones: signature(x = "dcCodaMCMC"): ...
str: signature(object = "dcCodaMCMC"): ...
summary: signature(object = "dcCodaMCMC"): ...

Author(s)

Peter Solymos

Examples

showClass("dcCodaMCMC")
showClass("dcCodaMCMC")

Class `"dcdiag"`

Description

Stands for the 'dcdiag' S3 class from dclone package.

Objects from the Class

A virtual Class: No objects may be created from it.

Extends

Class "dcDiag", directly.

Methods

No methods defined with class "dcdiag" in the signature.

Author(s)

Peter Solymos

Examples

showClass("dcdiag")
showClass("dcdiag")

Class `"dcDiag"`

Description

Virtual class for data cloning convergence diagnostics.

Objects from the Class

A virtual Class: No objects may be created from it.

Methods

No methods defined with class "dcDiag" in the signature.

Author(s)

Peter Solymos

Examples

showClass("dcDiag")
showClass("dcDiag")

Class `"dcFit"`

Description

Compendium for data cloning

Objects from the Class

Objects can be created by calls of the form new("dcFit", ...).

Slots

multiply:: Object of class "dcArgs", same as corresponding dc.fit argument.
unchanged:: Object of class "dcArgs", same as corresponding dc.fit argument.
update:: Object of class "dcArgs", same as corresponding dc.fit argument.
updatefun:: Object of class "dcFunction", same as corresponding dc.fit argument.
initsfun:: Object of class "dcFunction", same as corresponding dc.fit argument.
flavour:: Object of class "character", same as corresponding dc.fit argument, default is "jags". It can also be "winbugs", "openbugs", or "brugs" referring to the argument of bugs.fit, in which case flavour will be treated as "bugs".
data:: Object of class "list", same as corresponding dc.fit argument.
model:: Object of class "dcModel", same as corresponding dc.fit argument.
params:: Object of class "dcParams", same as corresponding dc.fit argument.
inits:: Object of class "dcInits", same as corresponding dc.fit argument.

Extends

Class "gsFit", directly.

Methods

show: signature(object = "dcFit"): ...

Author(s)

Peter Solymos

Examples

showClass("dcFit")
showClass("dcFit")

Class `"dcFunction"`

Description

Virtual class for BUGS/JAGS models defined as functions.

Objects from the Class

A virtual Class: No objects may be created from it.

Methods

No methods defined with class "dcFunction" in the signature.

Author(s)

Peter Solymos

Examples

showClass("dcFunction")
showClass("dcFunction")

Class `"dcInits"`

Description

Virtual class for initial values.

Objects from the Class

A virtual Class: No objects may be created from it.

Methods

No methods defined with class "dcInits" in the signature.

Author(s)

Peter Solymos

Examples

showClass("dcInits")
showClass("dcInits")

Asymptotic maximum likelihood estimation with data cloning

Description

This function is a wrapper to fit the model to the data and obtain MLE point estimates and asymptotic standard errors based on the estimate of the Fisher information matrix (theory given by Lele et al. 2007, 2010, software implementation is given in Solymos 2010).

Usage

dcmle(x, params, n.clones = 1, cl = NULL, nobs, ...)
dcmle(x, params, n.clones = 1, cl = NULL, nobs, ...)

Arguments

`x`	an object of class `"gsFit"` or `"dcFit"`.
`params`	character, vector of model parameters to monitor.
`n.clones`	integer, vector for the number of clones used in fitting.
`cl`	cluster object (snow type cluster) or number of cores (multicore type forking), optional.
`nobs`	number of observations, optional.
`...`	other arguments passed to underlying functions (see Details).

Details

The function uses slots of the input object and passes them as arguments to underlying functions (jags.fit, jags.parfit, bugs.fit, bugs.parfit, stan.fit, stan.parfit dc.fit, dc.parfit).

Value

An object of class "dcmle".

Author(s)

Peter Solymos

References

Solymos, P., 2010. dclone: Data Cloning in R. The R Journal 2(2), 29–37. URL: https://journal.r-project.org/archive/2010-2/RJournal_2010-2_Solymos.pdf

Lele, S.R., B. Dennis and F. Lutscher, 2007. Data cloning: easy maximum likelihood estimation for complex ecological models using Bayesian Markov chain Monte Carlo methods. Ecology Letters 10, 551–563.

Lele, S. R., K. Nadeem and B. Schmuland, 2010. Estimability and likelihood inference for generalized linear mixed models using data cloning. Journal of the American Statistical Association 105, 1617–1625.

Examples

## Data and model taken from Ponciano et al. 2009
## Ecology 90, 356-362.
paurelia <- c(17,29,39,63,185,258,267,392,510,
    570,650,560,575,650,550,480,520,500)
paramecium <- new("dcFit")
paramecium@data <- list(
    ncl=1,
    n=length(paurelia),
    Y=dcdim(data.matrix(paurelia)))
paramecium@model <- function() {
    for (k in 1:ncl) {
        for(i in 2:(n+1)){
            Y[(i-1), k] ~ dpois(exp(X[i, k])) # observations
            X[i, k] ~ dnorm(mu[i, k], 1 / sigma^2) # state
            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) # state at t0
    }
    beta ~ dlnorm(-1, 1) # Priors on model parameters
    sigma ~ dlnorm(0, 1)
    tmp ~ dlnorm(0, 1)
    lambda <- tmp + 1
    mu0 <- log(2)  + log(lambda) - log(1 + beta * 2)
}
paramecium@multiply <- "ncl"
paramecium@unchanged <- "n"
paramecium@params <- c("lambda","beta","sigma")
## Not run: 
(m1 <- dcmle(paramecium, n.clones=1, n.iter=1000))
(m2 <- dcmle(paramecium, n.clones=2, n.iter=1000))
(m3 <- dcmle(paramecium, n.clones=1:3, n.iter=1000))
cl <- makePSOCKcluster(3)
(m4 <- dcmle(paramecium, n.clones=2, n.iter=1000, cl=cl))
(m5 <- dcmle(paramecium, n.clones=1:3, n.iter=1000, cl=cl))
(m6 <- dcmle(paramecium, n.clones=1:3, n.iter=1000, cl=cl,
    partype="parchains"))
(m7 <- dcmle(paramecium, n.clones=1:3, n.iter=1000, cl=cl,
    partype="both"))
stopCluster(cl)

## End(Not run)
## Data and model taken from Ponciano et al. 2009
## Ecology 90, 356-362.
paurelia <- c(17,29,39,63,185,258,267,392,510,
    570,650,560,575,650,550,480,520,500)
paramecium <- new("dcFit")
paramecium@data <- list(
    ncl=1,
    n=length(paurelia),
    Y=dcdim(data.matrix(paurelia)))
paramecium@model <- function() {
    for (k in 1:ncl) {
        for(i in 2:(n+1)){
            Y[(i-1), k] ~ dpois(exp(X[i, k])) # observations
            X[i, k] ~ dnorm(mu[i, k], 1 / sigma^2) # state
            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) # state at t0
    }
    beta ~ dlnorm(-1, 1) # Priors on model parameters
    sigma ~ dlnorm(0, 1)
    tmp ~ dlnorm(0, 1)
    lambda <- tmp + 1
    mu0 <- log(2)  + log(lambda) - log(1 + beta * 2)
}
paramecium@multiply <- "ncl"
paramecium@unchanged <- "n"
paramecium@params <- c("lambda","beta","sigma")
## Not run: 
(m1 <- dcmle(paramecium, n.clones=1, n.iter=1000))
(m2 <- dcmle(paramecium, n.clones=2, n.iter=1000))
(m3 <- dcmle(paramecium, n.clones=1:3, n.iter=1000))
cl <- makePSOCKcluster(3)
(m4 <- dcmle(paramecium, n.clones=2, n.iter=1000, cl=cl))
(m5 <- dcmle(paramecium, n.clones=1:3, n.iter=1000, cl=cl))
(m6 <- dcmle(paramecium, n.clones=1:3, n.iter=1000, cl=cl,
    partype="parchains"))
(m7 <- dcmle(paramecium, n.clones=1:3, n.iter=1000, cl=cl,
    partype="both"))
stopCluster(cl)

## End(Not run)

Class `"dcmle"`

Description

Fitted model object from dcmle.

Objects from the Class

Objects can be created by calls of the form new("dcmle", ...).

Slots

call:: Object of class "language", the call.
coef:: Object of class "numeric", coefficients (posterior means).
fullcoef:: Object of class "numeric", full coefficients, possibly with fixed values.
vcov:: Object of class "matrix", variance covariance matrix.
details:: Object of class "dcCodaMCMC", the fitted model object.
nobs:: Object of class "integer", number of observations, optional.
method:: Object of class "character".

Methods

[: signature(x = "dcmle"): ...
[[: signature(x = "dcmle"): ...
acfplot: signature(x = "dcmle"): ...
as.array: signature(x = "dcmle"): ...
as.matrix: signature(x = "dcmle"): ...
as.mcmc.list: signature(x = "dcmle"): ...
autocorr.diag: signature(mcmc.obj = "dcmle"): ...
chanames: signature(x = "dcmle"): ...
chisq.diag: signature(x = "dcmle"): ...
coef: signature(object = "dcmle"): ...
coerce: signature(from = "codaMCMC", to = "dcmle"): ...
coerce: signature(from = "dcCodaMCMC", to = "dcmle"): ...
coerce: signature(from = "dcmle", to = "codaMCMC"): ...
coerce: signature(from = "dcmle", to = "dcCodaMCMC"): ...
coerce: signature(from = "dcmle", to = "MCMClist"): ...
coerce: signature(from = "MCMClist", to = "dcmle"): ...
confint: signature(object = "dcmle"): ...
crosscorr.plot: signature(x = "dcmle"): ...
crosscorr: signature(x = "dcmle"): ...
cumuplot: signature(x = "dcmle"): ...
dcdiag: signature(x = "dcmle"): ...
dcsd: signature(object = "dcmle"): ...
dctable: signature(x = "dcmle"): ...
densityplot: signature(x = "dcmle"): ...
densplot: signature(x = "dcmle"): ...
end: signature(x = "dcmle"): ...
frequency: signature(x = "dcmle"): ...
gelman.diag: signature(x = "dcmle"): ...
gelman.plot: signature(x = "dcmle"): ...
geweke.diag: signature(x = "dcmle"): ...
head: signature(x = "dcmle"): ...
heidel.diag: signature(x = "dcmle"): ...
lambdamax.diag: signature(x = "dcmle"): ...
mcpar: signature(x = "dcmle"): ...
nchain: signature(x = "dcmle"): ...
nclones: signature(x = "dcmle"): ...
niter: signature(x = "dcmle"): ...
nvar: signature(x = "dcmle"): ...
pairs: signature(x = "dcmle"): ...
plot: signature(x = "dcmle", y = "missing"): ...
qqmath: signature(x = "dcmle"): ...
quantile: signature(x = "dcmle"): ...
raftery.diag: signature(x = "dcmle"): ...
show: signature(object = "dcmle"): ...
stack: signature(x = "dcmle"): ...
start: signature(x = "dcmle"): ...
str: signature(object = "dcmle"): ...
summary: signature(object = "dcmle"): ...
tail: signature(x = "dcmle"): ...
thin: signature(x = "dcmle"): ...
time: signature(x = "dcmle"): ...
traceplot: signature(x = "dcmle"): ...
update: signature(object = "dcmle"): ...
varnames: signature(x = "dcmle"): ...
vcov: signature(object = "dcmle"): ...
window: signature(x = "dcmle"): ...
xyplot: signature(x = "dcmle"): ...

Author(s)

Peter Solymos

Examples

showClass("dcmle")
showClass("dcmle")

Class `"dcModel"`

Description

Virtual class for BUGS/JAGS models.

Objects from the Class

A virtual Class: No objects may be created from it.

Methods

No methods defined with class "dcModel" in the signature.

Author(s)

Peter Solymos

Examples

showClass("dcModel")
showClass("dcModel")

Class `"dcParams"`

Description

Virtual class for model parameters to monitor.

Objects from the Class

A virtual Class: No objects may be created from it.

Methods

No methods defined with class "dcParams" in the signature.

Author(s)

Peter Solymos

Examples

showClass("dcParams")
showClass("dcParams")

Class `"dctable"`

Description

Stands for the 'dctable' S3 class from dclone package.

Objects from the Class

A virtual Class: No objects may be created from it.

Extends

Class "dcTable", directly.

Methods

No methods defined with class "dctable" in the signature.

Author(s)

Peter Solymos

Examples

showClass("dctable")
showClass("dctable")

Class `"dcTable"`

Description

Posterior statistics from iterative fit, virtual class.

Objects from the Class

A virtual Class: No objects may be created from it.

Methods

No methods defined with class "dcTable" in the signature.

Author(s)

Peter Solymos

Examples

showClass("dcTable")
showClass("dcTable")

Diagnostic functions set as generic

Description

Diagnostic functions set as generic.

Usage

gelman.diag(x, ...)
geweke.diag(x, ...)
heidel.diag(x, ...)
raftery.diag(x, ...)

gelman.plot(x, ...)
gelman.diag(x, ...)
geweke.diag(x, ...)
heidel.diag(x, ...)
raftery.diag(x, ...)

gelman.plot(x, ...)

Arguments

`x`	MCMC objects.
`...`	Other arguments.

Details

Diagnostic functions from coda package are defined as generics for extensibility.

Value

Diagnostics summaries, and plot.

Author(s)

Peter Solymos

References

See relevant help pages.

Class `"gsFit"`

Description

BUGS/JAGS compendium

Objects from the Class

Objects can be created by calls of the form new("gsFit", ...).

Slots

data:: Object of class "list", same as corresponding jags.fit bugs.fit or argument.
model:: Object of class "dcModel", same as corresponding jags.fit bugs.fit or argument.
params:: Object of class "dcParams", same as corresponding jags.fit bugs.fit or argument.
inits:: Object of class "dcInits", same as corresponding jags.fit bugs.fit or argument.
flavour:: Object of class "character", same as corresponding dc.fit argument, default is "jags". It can also be "winbugs", "openbugs", or "brugs" referring to the argument of bugs.fit, in which case flavour will be treated as "bugs".

Methods

show: signature(object = "gsFit"): ...

Author(s)

Peter Solymos

Examples

showClass("gsFit")
showClass("gsFit")

Data object creators

Description

Creator functions for data types used in the dcmle package.

Usage

makeGsFit(data, model, params = NULL, inits = NULL, flavour)

makeDcFit(data, model, params=NULL, inits = NULL,
    multiply = NULL, unchanged = NULL, update = NULL,
    updatefun = NULL, initsfun = NULL, flavour)
makeGsFit(data, model, params = NULL, inits = NULL, flavour)

makeDcFit(data, model, params=NULL, inits = NULL,
    multiply = NULL, unchanged = NULL, update = NULL,
    updatefun = NULL, initsfun = NULL, flavour)

Arguments

`data`	usually a named list with data.
`model`	BUGS model (function, character vector or a `custommodel` object). The argument is coerced into a `custommodel` object.
`params`	optional, character vector for model parameters to monitor.
`inits`	initial values (`NULL`, list or function).
`multiply`	optional, argument passed to `dc.fit`.
`unchanged`	optional, argument passed to `dc.fit`.
`update`	optional, argument passed to `dc.fit`.
`updatefun`	optional, argument passed to `dc.fit`.
`initsfun`	optional, argument passed to `dc.fit`.
`flavour`	optional, argument passed to `dc.fit`.

Details

'gsFit' (after BU*GS*/JA*GS*) is a basic object class representing requirements for the Bayesian MCMC model fitting. The 'dcFit' object class extends 'gsFit' by additional slots that are used to fine tune how data cloning is done during fitting process. Both 'gsFit' and 'dcFit' represent prerequisites for model fitting, but do not containing any fitted parts. Creator functions makeGsFit and makeDcFit are available for these classes. See dcmle-package help page for usage of creator functions.

The default flavour is stored in getOption("dcmle.flavour") with value "jags". It can be changed as options("dcmle.flavour"="bugs") if required.

Value

makeGsFit returns a 'gsFit' object (gsFit-class).

makeDcFit returns a 'dcFit' object (dcFit-class).

Author(s)

Peter Solymos

Examples

showClass("gsFit")
new("gsFit")
showClass("dcFit")
new("dcFit")
showClass("gsFit")
new("gsFit")
showClass("dcFit")
new("dcFit")

Class `"mcmc"`

Description

Stands for the 'mcmc' S3 class from coda package.

Objects from the Class

A virtual Class: No objects may be created from it.

Extends

Class "MCMClist", directly.

Methods

No methods defined with class "mcmc" in the signature.

Author(s)

Peter Solymos

Examples

showClass("mcmc")
showClass("mcmc")

Class `"mcmc.list"`

Description

Stands for the 'mcmc.list' S3 class from coda package.

Objects from the Class

A virtual Class: No objects may be created from it.

Extends

Class "MCMClist", directly.

Methods

No methods defined with class "mcmc.list" in the signature.

Author(s)

Peter Solymos

Examples

showClass("mcmc.list")
showClass("mcmc.list")

Class `"mcmc.list.dc"`

Description

Stands for the 'mcmc.list.dc' S3 class from dclone package.

Objects from the Class

A virtual Class: No objects may be created from it.

Extends

Class "MCMClist", directly.

Methods

No methods defined with class "mcmc.list.dc" in the signature.

Author(s)

Peter Solymos

Examples

showClass("mcmc.list.dc")
showClass("mcmc.list.dc")

Class `"MCMClist"`

Description

Virtual class for S3 mcmc.list object from coda package.

Objects from the Class

A virtual Class: No objects may be created from it.

Methods

acfplot: signature(x = "MCMClist"): ...
autocorr.diag: signature(mcmc.obj = "MCMClist"): ...
chanames: signature(x = "MCMClist"): ...
chisq.diag: signature(x = "MCMClist"): ...
coerce: signature(from = "codaMCMC", to = "MCMClist"): ...
coerce: signature(from = "dcCodaMCMC", to = "MCMClist"): ...
coerce: signature(from = "dcmle", to = "MCMClist"): ...
coerce: signature(from = "MCMClist", to = "codaMCMC"): ...
coerce: signature(from = "MCMClist", to = "dcCodaMCMC"): ...
coerce: signature(from = "MCMClist", to = "dcmle"): ...
confint: signature(object = "MCMClist"): ...
crosscorr.plot: signature(x = "MCMClist"): ...
crosscorr: signature(x = "MCMClist"): ...
cumuplot: signature(x = "MCMClist"): ...
densityplot: signature(x = "MCMClist"): ...
densplot: signature(x = "MCMClist"): ...
frequency: signature(x = "MCMClist"): ...
gelman.diag: signature(x = "MCMClist"): ...
gelman.plot: signature(x = "MCMClist"): ...
geweke.diag: signature(x = "MCMClist"): ...
heidel.diag: signature(x = "MCMClist"): ...
lambdamax.diag: signature(x = "MCMClist"): ...
mcpar: signature(x = "MCMClist"): ...
nchain: signature(x = "MCMClist"): ...
niter: signature(x = "MCMClist"): ...
nvar: signature(x = "MCMClist"): ...
pairs: signature(x = "MCMClist"): ...
plot: signature(x = "MCMClist", y = "missing"): ...
qqmath: signature(x = "MCMClist"): ...
quantile: signature(x = "MCMClist"): ...
raftery.diag: signature(x = "MCMClist"): ...
thin: signature(x = "MCMClist"): ...
traceplot: signature(x = "MCMClist"): ...
varnames: signature(x = "MCMClist"): ...
xyplot: signature(x = "MCMClist"): ...

Author(s)

Peter Solymos

Examples

showClass("MCMClist")
showClass("MCMClist")

Class `"nClones"`

Description

Number of clones, virtual class.

Objects from the Class

A virtual Class: No objects may be created from it.

Methods

No methods defined with class "nClones" in the signature.

Author(s)

Peter Solymos

Examples

showClass("nClones")
showClass("nClones")

Class `"summary.codaMCMC"`

Description

Summary object.

Objects from the Class

Objects can be created by calls of the form new("summary.codaMCMC", ...).

Slots

settings:: Object of class "integer", MCMC settings.
coef:: Object of class "matrix", posterior statistics.

Methods

show: signature(object = "summary.codaMCMC"): ...

Author(s)

Peter Solymos

Examples

showClass("summary.codaMCMC")
showClass("summary.codaMCMC")

Class `"summary.dcCodaMCMC"`

Description

Summary object.

Objects from the Class

Objects can be created by calls of the form new("summary.dcCodaMCMC", ...).

Slots

settings:: Object of class "integer", MCMC settings.
coef:: Object of class "matrix", coefficients (posterior means).
convergence:: Object of class "dcDiag", data cloning convergence diagnostics.

Extends

Class "summary.codaMCMC", directly.

Methods

show: signature(object = "summary.dcCodaMCMC"): ...

Author(s)

Peter Solymos

Examples

showClass("summary.dcCodaMCMC")
showClass("summary.dcCodaMCMC")

Class `"summary.dcmle"`

Description

Summary object.

Objects from the Class

Objects can be created by calls of the form new("summary.dcmle", ...).

Slots

title:: Object of class "character", title to print, optional.
call:: Object of class "language", the call.
settings:: Object of class "integer", MCMC settings.
coef:: Object of class "matrix", coefficients (posterior means).
convergence:: Object of class "dcDiag", data cloning convergence diagnostics.

Extends

Class "summary.dcCodaMCMC", directly. Class "summary.codaMCMC", by class "summary.dcCodaMCMC", distance 2.

Methods

show: signature(object = "summary.dcmle"): ...

Author(s)

Peter Solymos

Examples

showClass("summary.dcmle")
showClass("summary.dcmle")

Package 'dcmle'

Help Index

Hierarchical Models Made Easy with Data Cloning

Description

Details

Author(s)

References

See Also

Examples

coda package related generic functions

Description

Usage

Arguments

Value

Author(s)

See Also

Class "codaMCMC"

Description

Objects from the Class

Slots

Methods

Author(s)

See Also

Examples

Generic after similar coda function

Description

Usage

Arguments

Value

Author(s)

See Also

Generic after similar coda function

Description

Usage

Arguments

Value

Author(s)

See Also

Class "custommodel"

Description

Objects from the Class

Extends

Methods

Author(s)

See Also

Examples

Class "dcArgs"

Description

Objects from the Class

Methods

Author(s)

Examples

Class "dcCodaMCMC"

Description

Objects from the Class

Slots

Extends

Methods

Author(s)

See Also

Examples

Class "dcdiag"

Description

Objects from the Class

Extends

Methods

Author(s)

See Also

Examples

Class "dcDiag"

Description

Objects from the Class

Methods

Author(s)

See Also

Examples

Class "dcFit"

Description

Objects from the Class

Slots

Class `"codaMCMC"`

Class `"custommodel"`

Class `"dcArgs"`

Class `"dcCodaMCMC"`

Class `"dcdiag"`

Class `"dcDiag"`

Class `"dcFit"`

Class `"dcFunction"`

Class `"dcInits"`

Class `"dcmle"`

Class `"dcModel"`

Class `"dcParams"`

Class `"dctable"`

Class `"dcTable"`