dalmatian (version 0.3.0)

dalmatian: Run DGLM in JAGS via rjags

Description

The primary function which automates the running of JAGS.

Usage

dalmatian(df, mean.model, variance.model, jags.model.args, coda.samples.args,
  response = NULL, rounding = FALSE, lower = NULL, upper = NULL,
  parameters = NULL, svd = TRUE, residuals = FALSE, gencode = NULL,
  drop.levels = TRUE, drop.missing = TRUE, overwrite = FALSE,
  debug = FALSE, saveJAGSinput = NULL)

Arguments

df

Data frame containing the response and predictor values for each individual. (data.frame)

mean.model

Model list specifying the structure of the mean. (list)

variance.model

Model list specifying the structure of the variance. (list)

jags.model.args

List containing named arguments of jags.model. (list)

coda.samples.args

List containing named arguments of coda.samples. (list)

response

Name of variable in the data frame representing the response. (character)

rounding

Specifies that response has been rounded if TRUE. (logical)

lower

Name of variable in the data frame representing the lower bound on the response if rounded. (character)

upper

Name of variable in the data frame representing the upper bound on the response if rounded. (character)

parameters

Names of parameters to monitor. If NULL then default values are selected. (character)

svd

Compute Singular Variable Decomposition of model matrices to improve convergence. (logical)

residuals

If TRUE then compute residuals in output. (logical)

gencode

If TRUE then generate code potentially overwriting existing model file. By default generate code if the file does not exist and prompt user if it does. (logical)

drop.levels

If TRUE then drop unused levels from all factors in df. (logical)

drop.missing

If TRUE then remove records with missing response variable. (logical)

overwrite

If TRUE then overwrite existing JAGS files (non-interactive sessions only). (logical)

debug

If TRUE then enter debug model. (logical)

saveJAGSinput

Directory to which jags.model input is saved prior to calling jags.model(). This is useful for debugging. No files saved if NULL. (character)

Value

samples (mcmc.list)

Details

The primary function in the package, dalmatian automates the generation of code, data, and initial values. These are then passed as arguments to function from the rjags package which automates the generation of samplse from the posterior.

Examples

# NOT RUN {
# }
# NOT RUN {
## Load pied flycatcher data
data(pied_flycatchers_1)

## Create variables bounding the true load
pfdata$lower=ifelse(pfdata$load==0,log(.001),log(pfdata$load-.049))
pfdata$upper=log(pfdata$load+.05)
## Mean model
mymean=list(fixed=list(name="alpha",
                      formula=~ log(IVI) + broodsize + sex,
                      priors=list(c("dnorm",0,.001))))

## Variance model
myvar=list(fixed=list(name="psi",
                      link="log",
                      formula=~broodsize + sex,
                      priors=list(c("dnorm",0,.001))))

## Set working directory
## By default uses a system temp directory. You probably want to change this.
workingDir <- tempdir()

## Define list of arguments for jags.model()
jm.args <- list(file=file.path(workingDir,"pied_flycatcher_1_jags.R"),n.adapt=1000)

## Define list of arguments for coda.samples()
cs.args <- list(n.iter=5000)

## Run the model using dalmatian
pfresults <- dalmatian(df=pfdata,
                         mean.model=mymean,
                         variance.model=myvar,
                         jags.model.args=jm.args,
                         coda.samples.args=cs.args,
                         rounding=TRUE,
                         lower="lower",
                         upper="upper",
                         debug=FALSE)
# }