predict-est.jumpRegression-method: Prediction for a regression model dependent on a Poisson... In SimoneHermann/BaPreStoPro: Bayesian Prediction of Stochastic Processes

Description

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)).

Usage

 ```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) ```

Arguments

 `object` class object of MCMC samples: "est.jumpRegression", created with method `estimate,jumpRegression-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, `M2pred` variables will be predicted with the observation time distances `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

References

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.

Examples

 ``` 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) ```

SimoneHermann/BaPreStoPro documentation built on May 10, 2017, 1:42 p.m.