plotTimeSeriesResults {BayesianTools} | R Documentation |
Creates a time series plot typical for an MCMC / SMC fit
plotTimeSeriesResults(sampler, model, observed, error = NULL, plotResiduals = TRUE, start = 1, prior = FALSE, ...)
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 |
plotResiduals |
logical determining whether residuals should be plotted |
start |
numeric start value for the plot (see |
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 |
Florian Hartig
# 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))