multiModelTP | R Documentation |
This function takes an isotopeData class object and calculates by default three Bayesian models: one and two baselines without carbon fractionation and two baselines with carbon fractionation.
This function takes an isotopeData class object and calculates by default three Bayesian models: one and two baselines without carbon fractionation and two baselines with carbon fractionation.
multiModelTP( siData = siData, lambda = 2, n.chains = 2, n.adapt = 20000, n.iter = 20000, burnin = 20000, thin = 10, models = c("oneBaseline", "twoBaselines", "twoBaselinesFull"), params = NULL, print = FALSE, quiet = FALSE, ... ) multiModelTP( siData = siData, lambda = 2, n.chains = 2, n.adapt = 20000, n.iter = 20000, burnin = 20000, thin = 10, models = c("oneBaseline", "twoBaselines", "twoBaselinesFull"), params = NULL, print = FALSE, quiet = FALSE, ... )
siData |
an isotopeData class object. |
lambda |
numerical value, represents the trophic level of 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. |
models |
string or list representing Bayesian models. At the moment they can be "oneBaseline", "twoBaselines" and/or "twoBaselinesFull". |
params |
aditional parameters included as a list. |
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. |
For each model calculated, returns a data frame of 4 elements with raw posterior samples, a list with posterior TP samples, a list with posterior muDeltaN (if one baseline model was chosen) or alpha (if a two baselines model was chosen) and a data frame with a summary of posterior samples named gg.
For each model calculated, returns a data frame of 4 elements with raw posterior samples, a list with posterior TP samples, a list with posterior muDeltaN (if one baseline model was chosen) or alpha (if a two baselines model was chosen) and a data frame with a summary of posterior samples named gg.
## Not run: isotopeData <- generateTPData() models <- multiModelTP(isotopeData, n.adapt = 500, n.iter = 500, burnin = 500) credibilityIntervals(models$gg, x = "model") ## End(Not run) ## Not run: isotopeData <- generateTPData() models <- multiModelTP(isotopeData, n.adapt = 500, n.iter = 500, burnin = 500) credibilityIntervals(models$gg, x = "model") ## End(Not run)
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