pred-Bayes.fit-method function

Bayesian prediction method for a class object Bayes.fit

Bayesian prediction methods

## S4 method for signature 'Bayes.fit'
pred(x, invariant = FALSE, level = 0.05,
  newwindow = FALSE, plot.pred = TRUE, plot.legend = TRUE, burnIn,
  thinning, only.interval = TRUE, sample.length = 500,
  cand.length = 100, trajectories = FALSE, ylim, xlab = "times",
  ylab = "X", col = 3, lwd = 2, ...)

Arguments

• x: Bayes.fit class
• invariant: logical(1), if TRUE, the initial value is from the invariant distribution $X_t~N(\alpha/\beta, \sigma^2/2\beta)$ for the OU and $X_t~\Gamma(2\alpha/\sigma^2, \sigma^2/2\beta)$ for the CIR process, if FALSE (default) X0 is fixed from the data starting points
• level: alpha for the predicion intervals, default 0.05
• newwindow: logical(1), if TRUE, a new window is opened for the plot
• plot.pred: logical(1), if TRUE, the results are depicted grafically
• plot.legend: logical(1), if TRUE, a legend is added to the plot
• burnIn: optional, if missing, the proposed value of the mixedsde.fit function is taken
• thinning: optional, if missing, the proposed value of the mixedsde.fit function is taken
• only.interval: logical(1), if TRUE, only prediction intervals are calculated, much faster than sampling from the whole predictive distribution
• sample.length: number of samples to be drawn from the predictive distribution, if only.interval = FALSE
• cand.length: number of candidates for which the predictive density is calculated, i.e. the candidates to be drawn from
• trajectories: logical(1), if TRUE, only trajectories are drawn from the point estimations instead of sampling from the predictive distribution, similar to the frequentist approach
• ylim: optional
• xlab: optional, default 'times'
• ylab: optional, default 'X'
• col: color for the prediction intervals, default 3
• lwd: linewidth for the prediction intervals, default 3
• ...: optional plot parameters

References

Dion, C., Hermann, S. and Samson, A. (2016). Mixedsde: a R package to fit mixed stochastic differential equations.