Description Usage Arguments References Examples
Bayesian prediction of a stochastic process dY_t = b(φ,t,Y_t)dt + s(γ,t,Y_t)dW_t + h(η,t,Y_t)dN_t.
1 2 3 4 5 6 | ## S4 method for signature 'est.jumpDiffusion'
predict(object, t, burnIn, thinning, Lambda.mat,
which.series = c("new", "current"), M2pred = 10, cand.length = 1000,
pred.alg = c("Trajectory", "Distribution", "simpleTrajectory",
"simpleBayesTrajectory"), pred.alg.N = c("Trajectory", "Distribution"),
candN = 0:5, sample.length, plot.prediction = TRUE)
|
object |
class object of MCMC samples: "est.jumpDiffusion", created with method |
t |
vector of time points to make predictions for |
burnIn |
burn-in period |
thinning |
thinning rate |
Lambda.mat |
matrix-wise definition of intensity rate function (makes it faster) |
which.series |
which series to be predicted, new one ("new") or further development of current one ("current") |
M2pred |
optional, if current series to be predicted and t missing, |
cand.length |
length of candidate samples (if method = "vector"), for jump diffusion |
pred.alg |
prediction algorithm, "Distribution", "Trajectory", "simpleTrajectory" or "simpleBayesTrajectory" |
pred.alg.N |
prediction algorithm, "Distribution", "Trajectory" |
candN |
vector of candidate area for differences of N, only if pred.alg.N = "Distribution" |
sample.length |
number of samples to be drawn, default is the number of posterior samples |
plot.prediction |
if TRUE, prediction intervals are plotted |
Hermann, S. (2016a). BaPreStoPro: an R Package for Bayesian Prediction of Stochastic Processes. SFB 823 discussion paper 28/16.
Hermann, S. (2016b). Bayesian Prediction for Stochastic Processes based on the Euler Approximation Scheme. SFB 823 discussion paper 27/16.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | model <- set.to.class("jumpDiffusion",
parameter = list(theta = 0.1, phi = 0.05, gamma2 = 0.1, xi = c(3, 1/4)),
Lambda = function(t, xi) (t/xi[2])^xi[1])
t <- seq(0, 1, by = 0.01)
data <- simulate(model, t = t, y0 = 0.5)
est_jd <- estimate(model, t, data, 2000)
plot(est_jd)
## Not run:
pred_jd <- predict(est_jd, Lambda.mat = function(t, xi) (t/xi[,2])^xi[,1])
pred_jd2 <- predict(est_jd, pred.alg = "Distribution", pred.alg.N = "Distribution",
Lambda.mat = function(t, xi) (t/xi[,2])^xi[,1])
est <- estimate(model, t[1:81], data = list(N = data$N[1:81], Y = data$Y[1:81]), 2000)
pred <- predict(est, t = t[81:101], which.series = "current",
Lambda.mat = function(t, xi) (t/xi[,2])^xi[,1])
lines(t, data$Y, type = "l", lwd = 2)
## End(Not run)
pred_jd4 <- predict(est_jd, pred.alg = "simpleTrajectory", sample.length = 100)
for(i in 1:100) lines(t[-1], pred_jd4$Y[i,], col = "grey")
pred_jd5 <- predict(est_jd, pred.alg = "simpleBayesTrajectory", sample.length = 100)
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