PLUE: Profiled Likelihood Uncertainty Estimation
In pse: Parameter Space Exploration with Latin Hypercubes

Description Usage Arguments Details References

Performs a likelihood-based uncertainty estimation on a model. This analysis consists on a Metropolis Monte Carlo exploration of the parameter space and subsequent profiling of model results based on the likelihood of the input parameters.

PLUE(model = NULL, factors, N, LL, start, res.names = NULL,
  method = c("internal", "mcmc"), opts = list(), nboot = 0,
  repetitions = 1, cl = NULL)

## S3 method for class 'PLUE'
print(x, ...)

## S3 method for class 'PLUE'
plot(x, ...)

`model`	The function to be run, representing the model or simulation.
`factors`	The names of the input variables (used for naming the 'data' data.frame and in plotting) Either a vector of strings or a single number representing the number of factors
`N`	The number of samples to be generated by the Metropolis algorithm.
`LL`	The POSITIVE Likelihood function to be used by the Metropolis algorithm. It must accept an array with length equal to the number of factors.
`start`	The initial point to be evaluated. Must have the same length as the number of factors.
`res.names`	Optional: what are the names of the model results? (Used mainly for plotting)
`method`	May be either "internal", which runs a naive and inneficient algorithm provided for test and didatic purposes, or "mcmc", which will run the `metrop` function from the `mcmc` package.
`opts`	Further options to be passed to the Metropolis function. See the help on `metrop`.
`nboot`	Number of bootstrap replicates for calculating the PRCC.
`repetitions`	The number of model repetitions to be run for a single data point. See the vignette on stochastic models for details
`cl`	Cluster generated with the “parallel” library. May be of any type supported. If a cluster is provided, the model will be run in parallel or distributed across the cluster via clusterApply. No load balancing is provided, so the model results are reproducible. NOTE: You should manually export ALL objects required for the model to run, including the model function itself. See the help on `clusterExport` on package `parallel` for details.
`x`	An LHS/PLUE object. For "tell", an incomplete LHS object (created with model=NULL)
`...`	Currently ignored