dalmatian (version 1.0.0)

residuals.dalmatian: Residuals method for dalmatian fitted objects

Description

Computes posterior summaries of the residuals for each observation. Summary statistics include the posterior mean and the upper and lower bounds of the 95 If the response is not rounded then the residuals can either be sampled as part of the MCMC or computed during post-processing. If computed as part of the MCMC then residuals() will simply summarize the posterior distributions. Otherwise, residuals() will compute the residuals and their posterior summaries. If the response is rounded then the residuals must be sampled when the MCMC sampler is run.

Usage

# S3 method for dalmatian
residuals(object, ...)

Arguments

object

Object of class dalmatian created by dalmatian().

...

Ignored

Value

Data frame containing original data augmented with posterior mean and lower and upper bounds of the 95 residual for each observation.

Examples

# NOT RUN {
## Here we rerun the first example in
## \code{vignettes(pied-flycatcher-1)} with \code{residuals = TRUE}
## in order to sample the residuals and then use the \code{residuals()}
## function to summarize the posterior distributions. This is necessary
## because the output is too large to store inside the package.

## 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)

##### Model 1 #####

## Mean model
mymean=list(fixed=list(name="alpha",
       formula=~ log(IVI) + broodsize + sex,
       priors=list(c("dnorm",0,.001))))

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


## Set working directory
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,thin=20)

## Run the model using dalmatian
pfresults <- dalmatian(df=pfdata,
                       mean.model=mymean,
                       dispersion.model=mydisp,
                       jags.model.args=jm.args,
                       coda.samples.args=cs.args,
                       rounding=TRUE,
                       lower="lower",
                       upper="upper",
                       n.cores = 3,
                       residuals = TRUE,
                       overwrite = TRUE,
                       debug=FALSE)
 
## summarize residuals
res.pfresults <- residuals(object = pfresults)
# }