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

In BayesianTools: General-Purpose MCMC and SMC Samplers and Tools for Bayesian Statistics

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

Description

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

Usage

plotTimeSeriesResults(
  sampler,
  model,
  observed,
  error = NULL,
  plotResiduals = TRUE,
  start = 1,
  prior = FALSE,
  ...
)

Arguments

sampler	Either a) a matrix b) an MCMC object (list or not), or c) an SMC object
model	function that calculates model predictions for a given parameter vector
observed	observed values as vector
error	function with signature f(mean, par) that generates observations with error (error = stochasticity according to what is assumed in the likelihood) 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. See example in VSEM
plotResiduals	logical determining whether residuals should be plotted
start	numeric start value for the plot (see getSample)
prior	if a prior sampler is implemented, setting this parameter to TRUE will draw model parameters from the prior instead of the posterior distribution
...	further arguments passed to plot

Author(s)

Florian Hartig

See Also

getPredictiveIntervals

Examples

# Create time series
ts <- VSEMcreatePAR(1:100)

# create fake "predictions"
pred <- ts + rnorm(length(ts), mean = 0, sd = 2)

# plot time series
par(mfrow=c(1,2))

plotTimeSeries(observed = ts, main="Observed")
plotTimeSeries(observed = ts, predicted = pred, main = "Observed and predicted")

par(mfrow=c(1,1))

BayesianTools documentation built on Feb. 16, 2023, 8:44 p.m.