plotTimeSeriesResults

Either a) a matrix b) an MCMC object (list or not), or c) an SMC object

sampler

function that calculates model predictions for a given parameter vector

model

observed

function with signature f(mean, par) that generates error expectations from mean model predictions. Par is a vector from the matrix with the parameter samples (full length). f needs to know which of these parameters are parameters of the error function

error

logical determining whether residuals should be plotted

plotResiduals

numeric start value for the plot (see <code><a rd-options="" href="/link/getSample?package=BayesianTools&version=0.1.0" data-mini-rdoc="BayesianTools::getSample">getSample</a></code>)

start

Creates a time series plot typical for an MCMC / SMC fit

General-purpose MCMC and SMC samplers, as well as plot and diagnostic functions for Bayesian statistics, with a particular focus on calibrating complex system models. Implemented samplers include various Metropolis MCMC variants (including adaptive and/or delayed rejection MH), the T-walk, two differential evolution MCMCs, two DREAM MCMCs, and a sequential Monte Carlo (SMC) particle filter.

Florian Hartig

BayesianTools

General-Purpose MCMC and SMC Samplers and Tools for Bayesian
Statistics

plotTimeSeriesResults function

numeric start value for the plot (see <code><a rd-options='' href='getSample'>getSample</a></code>)

plotTimeSeriesResults: Creates a time series plot typical for an MCMC / SMC fit

Description

Usage

Arguments