MQMF (version 0.1.1)

do_MCMC: do_MCMC conducts an MCMC using Gibbs within Metropolis

Description

do_MCMC conducts a Gibbs within Metropolis algorithm. One can define the number of chains, the burnin before candidate parameter vectors and associated probabilities are stored, the total number of candidate vectors to keep and the step or thinning rate between accepting a result into the Markov Chain. One needs to input three functions: 1) 'infunk' to calculate the likelihoods, 2) 'calcpred' used within 'infunk' to calculate the predicted values used to compare with the observed (found in 'obsdat'), and 3) 'priorcalc' used to calculate the prior probabilities of the various parameters in each trial. (N + burnin) * thinstep iterations are made in total although only N are stored. The jumping function uses random normal deviates (mean=0, sd=1) to combine with each parameter value (after multiplication by the specific scaling factor held in the scales argument). Determination of suitable scaling values is generally done empirically, perhaps by trialing a small number of iterations to begin with. Multiple chains would be usual and the thinstep would be larger eg. 128, 256, or 512, but it would take 8, 16, or 32 times longer, depending on the number of parameters, these numbers are for four parameters only. The scales are usually empirically set to obtain an acceptance rate between 20 - 40 It is also usual to run numerous diagnostic plots on the outputs to ensure convergence onto the final stationary distribution. There are three main loops: 1) total number of iterations (N + burnin)* thinstep, used by priorcalc, 2) thinstep/(number of parameters) so that at least all parameters are stepped through at least once (thinstep = np) before any combinations are considered for acceptance, this means that the true thinning rate is thinstep/np, and 3) the number of parameters loop, that steps through the np parameters varying each one, one at a time.

Usage

do_MCMC(
  chains,
  burnin,
  N,
  thinstep,
  inpar,
  infunk,
  calcpred,
  calcdat,
  obsdat,
  priorcalc,
  scales,
  ...
)

Arguments

chains

the number of independent MCMC chains produced

burnin

the number of steps made before candidate parameter vectors begin to be kept.

N

the total number of posterior parameter vectors retained

thinstep

the number of interations before a vector is kept; must be divisible by the number of parameters

inpar

the initial parameter values (usually log-transformed)

infunk

the function used to calculate the negative log-likelihood

calcpred

the function used by infunk to calculate the predicted values for comparison with obsdat; defaults to simpspm.

calcdat

the data used by calcpred to calculate the predicted values

obsdat

the observed data (on the same scale as the predicted, ie usually log-transformed), against with the predicted values are compared.

priorcalc

a function used to calculate the prior probability for each of the parameters in each trial parameter vector.

scales

The re-scaling factors for each parameter to convert the normal random deviates into +/- values on a scale that leads to acceptance rates of between 20 and 40percent.

...

needed by the simpspm function = calcpred

Value

a list of the result chains, the acceptance and rejection rates

Examples

# NOT RUN {
data(abdat); fish <- as.matrix(abdat) # to increase speed
param <- log(c(0.4,9400,3400,0.05))
N <- 500  # usually very, very  many more  10s of 1000s
result <- do_MCMC(chains=1,burnin=20,N=N,thinstep=8,inpar=param,
                  infunk=negLL,calcpred=simpspm,calcdat=fish,
                  obsdat=log(fish[,"cpue"]),priorcalc=calcprior,
                  scales=c(0.06,0.05,0.06,0.42))
# a thinstep of 8 is whofully inadequate, see the runs in the plots
cat("Acceptance Rate = ",result[[2]],"\n")
cat("Failure Rate    = ",result[[3]],"\n")
oldpar <- par(no.readonly=TRUE)
#plotprep(width=6,height=5,newdev=FALSE)
out <- result[[1]][[1]] # get the list containing the matrix
pairs(out[,1:4],col=rgb(1,0,0,1/5)) # adjust the 1/5 to suit N

parset(plots=c(1,2)) # Note the serial correlation in each trace
plot1(1:N,out[,1],ylab="r",xlab="Replicate",defpar=FALSE)
plot1(1:N,out[,2],ylab="K",xlab="Replicate",defpar=FALSE)
par(oldpar)
# }