Description Usage Arguments References
Bayesian prediction
1 2 3 4 5 6 |
x |
Bayes.fit class |
invariant |
logical(1), if TRUE, the initial value is from the invariant distribution X_t~N(α/β, σ^2/2β) for the OU and X_t~Γ(2α/σ^2, σ^2/2β) 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 |
Dion, C., Hermann, S. and Samson, A. (2016). Mixedsde: a R package to fit mixed stochastic differential equations.
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