dcmle (version 0.4-1)

dcmle: 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, ...)

Value

An object of class "dcmle".

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).

Author

Peter Solymos

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).

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.

See Also

For additional arguments: jags.fit, jags.parfit, bugs.fit, bugs.parfit, stan.fit, stan.parfit dc.fit, dc.parfit.

Object classes: "dcmle"

Creator functions makeGsFit and makeDcFit

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")
if (FALSE) {
(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)
}