Description Usage Arguments References Examples
Bayesian prediction of a regression model y_i = f(t_i, N_{t_i}, θ) + ε_i with N_t\sim Pois(Λ(t, ξ)), ε_i\sim N(0,γ^2\widetilde{s}(t)).
1 2 3 4 5 6 | ## S4 method for signature 'est.jumpRegression'
predict(object, t, only.interval = TRUE,
level = 0.05, burnIn, thinning, Lambda.mat, fun.mat,
which.series = c("new", "current"), M2pred = 10, cand.length = 1000,
pred.alg = c("Distribution", "simpleTrajectory", "simpleBayesTrajectory"),
sample.length, grid = 1e-05, plot.prediction = TRUE)
|
object |
class object of MCMC samples: "est.jumpRegression", created with method |
t |
vector of time points to make predictions for |
only.interval |
if TRUE: only calculation of prediction intervals |
level |
level of the prediction intervals |
burnIn |
burn-in period |
thinning |
thinning rate |
Lambda.mat |
matrix-wise definition of intensity rate function (makes it faster) |
fun.mat |
matrix-wise definition of regression 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 "simpleTrajectory" |
sample.length |
number of samples to be drawn, default is the number of posterior samples |
grid |
fineness degree of sampling approximation |
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 | t <- seq(0,1, by = 0.01)
cl <- set.to.class("jumpRegression", fun = function(t, N, theta) theta[1]*t + theta[2]*N,
parameter = list(theta = c(1,2), gamma2 = 0.1, xi = c(3, 1/4)),
Lambda = function(t, xi) (t/xi[2])^xi[1])
data <- simulate(cl, t = t)
est <- estimate(cl, t, data, 1000)
plot(est)
## Not run:
pred <- predict(est, Lambda.mat = function(t, xi) (t/xi[,2])^xi[,1],
fun.mat = function(t, N, theta) theta[,1]*t + theta[,2]*N)
## End(Not run)
pred <- predict(est, pred.alg = "simpleTrajectory", sample.length = 100)
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