fec_stan: Modelling of faecal egg count data (one-sample case)

Description Usage Arguments Details Value Author(s) See Also Examples

View source: R/fec_stan.R

Description

Models faecal egg counts data in a one-sample case with (zero-inflated) Poisson-gamma model formulation using Stan modelling language. It is computationally several-fold faster compared to conventional MCMC techniques. For the installation instruction of Stan, please read https://github.com/stan-dev/rstan/wiki/RStan-Getting-Started.

Usage

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fec_stan(fec, rawCounts = FALSE, CF = 50, zeroInflation = TRUE, 
  muPrior, kappaPrior, phiPrior, 
  nsamples = 2000, nburnin = 1000, thinning = 1, nchain = 2, 
  ncore = 1, adaptDelta = 0.95, saveAll = FALSE, verbose = FALSE)

Arguments

fec

vector of faecal egg counts

rawCounts

logical. If true, preFEC and postFEC correspond to raw counts (as counted on equipment). Otherwise they correspond to calculated epgs (raw counts times correction factor). Defaults to FALSE.

CF

a positive integer or a vector of positive integers. Correction factor(s)

zeroInflation

logical. If true, uses the model with zero-inflation. Otherwise uses the model without zero-inflation

muPrior

a list with hyper-prior information for the group mean epg parameter μ. The default prior is list(priorDist = "gamma",hyperpars=c(1,0.001)), i.e. a gamma distribution with shape 1 and rate 0.001, its 90% probability mass lies between 51 and 2996

kappaPrior

a list with hyper-prior information for the group dispersion parameter κ. The default prior is list(priorDist = "gamma",hyperpars=c(1,0.7)), i.e. a gamma distribution with shape 1 and rate 0.7, its 90% probability mass lies between 0.1 and 4.3 with a median of 1

phiPrior

a list with hyper-prior information for zero-inflation parameter. The default prior is list(priorDist = "beta",hyperpars=c(1,1)), i.e. a uniform prior between 0 and 1

nsamples

a positive integer specifying the number of samples for each chain (including burn-in samples)

nburnin

a positive integer specifying the number of burn-in samples

thinning

a positive integer specifying the thinning parameter, the period for saving samples

nchain

a positive integer specifying the number of chains

ncore

a positive integer specifying the number of cores to use when executing the chains in parallel

adaptDelta

the target acceptance rate, a numeric value between 0 and 1

saveAll

logical. If TRUE, posterior samples for all parameters are saved in the stanfit object. If FALSE, only samples for μ, κ and φ are saved. Default to FALSE.

verbose

logical. If true, prints progress and debugging information

Details

The first time each non-default model is applied, it can take up to 20 seconds for Stan to compile the model. Currently the function only support prior distributions with two parameters. For a complete list of supported priors and their parameterization, please consult the list of distributions in Stan http://mc-stan.org/documentation/.

The default number of samples per chain is 2000, with 1000 burn-in samples. Normally this is sufficient in Stan. If the chains do not converge, one should tune the MCMC parameters until convergence is reached to ensure reliable results.

Value

Prints out summary of meanEPG as the posterior mean epg. The posterior summary contains the mean, standard deviation (sd), 2.5%, 50% and 97.5% percentiles, the 95% highest posterior density interval (HPDLow95 and HPDHigh95) and the posterior mode. NOTE: we recommend to use the 95% HPD interval and the mode for further statistical analysis.

The returned value is a list that consists of:

stan.samples

An object of S4 class stanfit representing the fitted results. For more information, please see the stanfit-class in rstan reference manual.

posterior.summary

A data frame that is the same as the printed posterior summary.

Author(s)

Craig Wang

See Also

simData1s for simulating faecal egg count data with one sample

Examples

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## Not run: 
## load the sample data
data(epgs)

## apply zero-infation model
model <- fec_stan(epgs$before, rawCounts=FALSE, CF=50)
samples <- stan2mcmc(model$stan.samples)

## End(Not run)

eggCounts documentation built on May 2, 2018, 5:06 p.m.