fecr_stanSimple: Model the reduction of faecal egg count using a simple...
In eggCounts: Hierarchical Modelling of Faecal Egg Counts

fecr_stanSimple

R Documentation

Model the reduction of faecal egg count using a simple Bayesian model

Description

Models the reduction in faecal egg counts with a simple Bayesian model formulation. The model is for paired design only, and it assumes Poisson distribution for the observed egg counts.

Usage

fecr_stanSimple(preFEC, postFEC, rawCounts = FALSE, 
  preCF = 50, postCF = preCF, muPrior, deltaPrior, 
  nsamples = 2000, nburnin = 1000, thinning = 1, nchain = 2, 
  ncore = 1, adaptDelta = 0.95, saveAll = FALSE, verbose = FALSE)

Arguments

`preFEC`	numeric vector. Pre-treatment faecal egg counts.
`postFEC`	numeric vector. Post-treatment 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`.
`preCF`	positive integer or vector of positive integers. Pre-treatment correction factor(s).
`postCF`	positive integer or vector of positive integers. Post-treatment correction factor(s).
`muPrior`	named list. Prior for the group mean epg parameter `\mu`. 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.
`deltaPrior`	named list. Prior for the reduction parameter `\delta`. The default prior is `list(priorDist = "beta",hyperpars=c(1,1))`, i.e. a uniform prior between 0 and 1.
`nsamples`	a positive integer. Number of samples for each chain (including burn-in samples).
`nburnin`	a positive integer. Number of burn-in samples.
`thinning`	a positive integer. Thinning parameter, i.e. the period for saving samples.
`nchain`	a positive integer. Number of chains.
`ncore`	a positive integer. Number of cores to use when executing the chains in parallel.
`adaptDelta`	numeric. 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. Otherwise only samples for `\delta` and `\mu` are saved. Default to FALSE.
`verbose`	logical. If TRUE, prints progress and debugging information.

Details

The first time each model with non-default priors is applied, it can take up to 20 seconds 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 User Guide.

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 the posterior summary of FECR as the reduction, meanEPG.untreated as the mean pre-treatment epg, and meanEPG.treated as the mean after-treatment 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
`posterior.summary`	A data.frame that is the same as the printed posterior summary

Author(s)

Tea Isler
Craig Wang

Examples


## load sample data
data(epgs)

## apply paired model with individual efficacy
model <- fecr_stanSimple(epgs$before, epgs$after, 
            rawCounts = FALSE, preCF = 10)
samples <- stan2mcmc(model$stan.samples)

eggCounts documentation built on Oct. 15, 2023, 1:06 a.m.