dc.fit: Iterative model fitting with data cloning

View source: R/dc.fit.R

dc.fitR Documentation

Iterative model fitting with data cloning


jags.fit or bugs.fit is iteratively used to fit a model with increasing the number of clones.


dc.fit(data, params, model, inits, n.clones,
    multiply = NULL, unchanged = NULL,
    update = NULL, updatefun = NULL, initsfun = NULL,
    flavour = c("jags", "bugs", "stan"), n.chains = 3,
    return.all=FALSE, check.nclones=TRUE, ...)



A named list (or environment) containing the data.


Character vector of parameters to be sampled. It can be a list of 2 vectors, 1st element is used as parameters to monitor, the 2nd is used as parameters to use in calculating the data cloning diagnostics.


Character string (name of the model file), a function containing the model, or a custommodel object (see Examples).


Optional specification of initial values in the form of a list or a function (see Initialization at jags.model). If missing, will be treated as NULL and initial values will be generated automatically.


An integer vector containing the numbers of clones to use iteratively.


Numeric or character index for list element(s) in the data argument to be multiplied by the number of clones instead of repetitions.


Numeric or character index for list element(s) in the data argument to be left unchanged.


Character, the name of the list element(s) in the data argument that has to be updated by updatefun in each iteration. This usually is for making priors more informative, and enhancing convergence. See Details and Examples.


A function to use for updating named elements in data. It should take an 'mcmc.list' object as 1st argument, 2nd argument can be the number of clones. If legth(update) > 1 the function must return a named list so that data[update] can be updated. See Details and Examples.


A function to use for generating initial values, inits are updated by the object returned by this function from the second iteration. If initial values are not dependent on the previous iteration, this should be NULL, otherwise, it should take an 'mcmc.list' object as 1st argument, 2nd argument can be the number of clones. This feature is useful if latent nodes are provided in inits so it also requires to be cloned for subsequent iterations. See Details and Examples.


If "jags", the function jags.fit is called. If "bugs", the function bugs.fit is called. If "stan", the function stan.fit is called.


Number of chains to generate.


Logical. If TRUE, all the MCMC list objects corresponding to the sequence n.clones are returned for further inspection. Otherwise only the MCMC list corresponding to highest number of clones is returned with summary statistics for the rest.


Logical, whether to check and ensure that values of n.clones are unique and increasing. check.nclones = FALSE means that n.clones is used as is, thus it is possible to supply repeated values but still use the update functionality.


Other values supplied to jags.fit, or bugs.fit, depending on the flavour argument.


The function fits a JAGS/BUGS model with increasing numbers of clones, as supplied by the argument n.clones. Data cloning is done by the function dclone using the arguments multiply and unchanged. An updating function can be provided, see Examples.


An object inheriting from the class 'mcmc.list'.


Peter Solymos, solymos@ualberta.ca, implementation is based on many discussions with Khurram Nadeem and Subhash Lele.


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.

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

See Also

Data cloning: dclone.

Parallel computations: dc.parfit

Model fitting: jags.fit, bugs.fit

Convergence diagnostics: dctable, dcdiag


## Not run: 
## simulation for Poisson GLMM
n <- 20
beta <- c(2, -1)
sigma <- 0.1
alpha <- rnorm(n, 0, sigma)
x <- runif(n)
X <- model.matrix(~x)
linpred <- crossprod(t(X), beta) + alpha
Y <- rpois(n, exp(linpred))
## JAGS model as a function
jfun1 <- function() {
    for (i in 1:n) {
        Y[i] ~ dpois(lambda[i])
        log(lambda[i]) <- alpha[i] + inprod(X[i,], beta)
        alpha[i] ~ dnorm(0, 1/sigma^2)
    for (j in 1:np) {
        beta[j] ~ dnorm(0, 0.001)
    sigma ~ dlnorm(0, 0.001)
## data
jdata <- list(n = n, Y = Y, X = X, np = NCOL(X))
## inits with latent variable and parameters
ini <- list(alpha=rep(0,n), beta=rep(0, NCOL(X)))
## function to update inits
ifun <- function(model, n.clones) {
    list(alpha=dclone(rep(0,n), n.clones),
## iteartive fitting
jmod <- dc.fit(jdata, c("beta", "sigma"), jfun1, ini,
    n.clones = 1:5, multiply = "n", unchanged = "np",
## summary with DC SE and R hat
dct <- dctable(jmod)
## How to use estimates to make priors more informative?
glmm.model.up <- function() {
    for (i in 1:n) {
        Y[i] ~ dpois(lambda[i])
        log(lambda[i]) <- alpha[i] + inprod(X[i,], beta[1,])
        alpha[i] ~ dnorm(0, 1/sigma^2)
    for (j in 1:p) {
        beta[1,j] ~ dnorm(priors[j,1], priors[j,2])
    sigma ~ dgamma(priors[(p+1),2], priors[(p+1),1])
## function for updating, x is an MCMC object
upfun <- function(x) {
    if (missing(x)) {
        p <- ncol(X)
        return(cbind(c(rep(0, p), 0.001), rep(0.001, p+1)))
    } else {
        par <- coef(x)
        return(cbind(par, rep(0.01, length(par))))
updat <- list(n = n, Y = Y, X = X, p = ncol(X), priors = upfun())
dcmod <- dc.fit(updat, c("beta", "sigma"), glmm.model.up,
    n.clones = 1:5, multiply = "n", unchanged = "p",
    update = "priors", updatefun = upfun)
## time series example
## data and model taken from Ponciano et al. 2009
## Ecology 90, 356-362.
paurelia <- c(17,29,39,63,185,258,267,392,510,
dat <- list(ncl=1, n=length(paurelia), Y=dcdim(data.matrix(paurelia)))
beverton.holt <- function() {
    for (k in 1:ncl) {
        for(i in 2:(n+1)){
            ## observations
            Y[(i-1), k] ~ dpois(exp(X[i, k]))
            ## state
            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]))
        ## state at t0
        X[1, k] ~ dnorm(mu0, 1 / sigma^2)
    # Priors on model parameters
    beta ~ dlnorm(-1, 1)
    sigma ~ dlnorm(0, 1)
    tmp ~ dlnorm(0, 1)
    lambda <- tmp + 1
    mu0 <- log(2)  + log(lambda) - log(1 + beta * 2)
mod <- dc.fit(dat, c("lambda","beta","sigma"), beverton.holt,
    n.clones=c(1, 2, 5, 10), multiply="ncl", unchanged="n")
## compare with results from the paper:
##   beta   = 0.00235
##   lambda = 2.274
##   sigma  = 0.1274

## Using WinBUGS/OpenBUGS
dat <- list(J = nrow(schools), y = schools$estimate,
    sigma.y = schools$sd)
bugs.model <- function(){
       for (j in 1:J){
         y[j] ~ dnorm (theta[j], tau.y[j])
         theta[j] ~ dnorm (mu.theta, tau.theta)
         tau.y[j] <- pow(sigma.y[j], -2)
       mu.theta ~ dnorm (0.0, 1.0E-6)
       tau.theta <- pow(sigma.theta, -2)
       sigma.theta ~ dunif (0, 1000)
inits <- function(){
    list(theta=rnorm(nrow(schools), 0, 100), mu.theta=rnorm(1, 0, 100),
         sigma.theta=runif(1, 0, 100))
param <- c("mu.theta", "sigma.theta")
if (.Platform$OS.type == "windows") {
sim2 <- dc.fit(dat, param, bugs.model, n.clones=1:2,
    flavour="bugs", program="WinBUGS", multiply="J",
    n.iter=2000, n.thin=1)
sim3 <- dc.fit(dat, param, bugs.model, n.clones=1:2,
    flavour="bugs", program="brugs", multiply="J",
    n.iter=2000, n.thin=1)
sim4 <- dc.fit(dat, param, bugs.model, n.clones=1:2,
    flavour="bugs", program="openbugs", multiply="J",
    n.iter=2000, n.thin=1)

## Using Stan
if (require(rstan)) {
    model <- custommodel("data {
          int<lower=0> N;
          vector[N] y;
          vector[N] x;
        parameters {
          real alpha;
          real beta;
          real<lower=0> sigma;
        model {
          alpha ~ normal(0,10);
          beta ~ normal(0,10);
          sigma ~ cauchy(0,5);
          for (n in 1:N)
            y[n] ~ normal(alpha + beta * x[n], sigma);
    N <- 100
    alpha <- 1
    beta <- -1
    sigma <- 0.5
    x <- runif(N)
    y <- rnorm(N, alpha + beta * x, sigma)
    dat <- list(N=N, y=y, x=x)
    params <- c("alpha", "beta", "sigma")
    ## compile on 1st time only
    fit0 <- stan.fit(dat, params, model)
    ## reuse compiled fit0
    dcfit <- dc.fit(dat, params, model, n.clones=1:2,
        flavour="stan", multiply="N", fit=fit0)

## End(Not run)

datacloning/dclone documentation built on Jan. 7, 2023, 2:38 p.m.