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 PLUE
print(x, ...)
# S3 method for PLUE
plot(x, ...)
The function to be run, representing the model or simulation.
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
The number of samples to be generated by the Metropolis algorithm.
The POSITIVE Likelihood function to be used by the Metropolis algorithm. It must accept an array with length equal to the number of factors.
The initial point to be evaluated. Must have the same length as the number of factors.
Optional: what are the names of the model results? (Used mainly for plotting)
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.
Further options to be passed to the Metropolis function. See the help on
metrop.
Number of bootstrap replicates for calculating the PRCC.
The number of model repetitions to be run for a single data point. See the vignette on stochastic models for details
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.
An LHS/PLUE object. For "tell", an incomplete LHS object (created with model=NULL)
Currently ignored
A detailed description can be found on Chalom & Prado (2015).
Chalom, A. and Prado, P.I.K.L. 2015. Uncertainty analysis and composite hypothesis under the likelihood paradigm. arXiv:1508.03354 [q-bio.QM]