tRophicPosition (version 0.8.0)

multiSpeciesTP: Multiple species calculation of trophic position

Description

This function takes a named list of isotopeData class objects and calculates one or more Bayesian models of trophic position for each element of the list.

Usage

multiSpeciesTP(
  siDataList = siDataList,
  lambda = 2,
  n.chains = 2,
  n.adapt = 20000,
  n.iter = 20000,
  burnin = 20000,
  thin = 10,
  model = "oneBaseline",
  print = FALSE,
  quiet = FALSE,
  ...
)

Value

A list of 4 elements. The output is organised as lists nested. The first element (multiSpeciesTP) has the gg data frame returned by multiModelTP, the second element (df) is a data frame with summary information for all consumers and models, the third element (TPs) has the raw posterior trophic position for all consumers and models, and the last element (Alphas) has raw posterior of muDeltaN (if one baseline model was chosen) or alpha (if a two baselines model was chosen) for all consumers and models.

Arguments

siDataList

a named list of isotopeData class objects.

lambda

numerical value, represents the trophic level for baseline(s).

n.chains

number of parallel chains for the model. If convergence diagnostics (such as Gelman-Rubin) are printed, n.chains needs to be >= 2.

n.adapt

number of adaptive iterations, before the actual sampling.

n.iter

number of iterations for Bayesian modelling (posterior sampling).

burnin

number of iterations discarded as burn in.

thin

thinning. Number of samples discarded while performing posterior sampling.

model

string or list representing Bayesian models. At the moment they can be "oneBaseline", "twoBaselines" and/or "twoBaselinesFull".

print

logical value to indicate whether Gelman and Rubin's convergence diagnostic and summary of samples are printed.

quiet

logical value to indicate whether messages generated during compilation will be suppressed, as well as the progress bar during adaptation.

...

additional arguments passed to this function.

Examples

# \donttest{
siDataList <- list("consumer1" = generateTPData(consumer = "consumer1"),
"consumer2" = generateTPData(consumer = "consumer2"))
models <- multiSpeciesTP(siDataList, model = "twoBaselines", n.adapt = 500,
n.iter = 500, burnin = 500)
credibilityIntervals(models$df, x = "consumer")
# }