# 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

## Description

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

## Usage

 ```1 2``` ```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

`getPredictiveIntervals`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13``` ```# 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)) ```