# predict-est.NHPP-method: Prediction for a non-homogeneous Poisson process In SimoneHermann/BaPreStoPro: Bayesian Prediction of Stochastic Processes

## Description

Bayesian prediction of a non-homogeneous Poisson process with cumulative intensity function Λ(t, ξ).

## Usage

 ```1 2 3 4 5 6 7``` ```## S4 method for signature 'est.NHPP' predict(object, variable = c("eventTimes", "PoissonProcess"), t, burnIn, thinning, Lambda.mat, which.series = c("new", "current"), Tstart, M2pred = 10, rangeN = c(0, 5), cand.length = 1000, pred.alg = c("Trajectory", "Distribution", "simpleTrajectory", "simpleBayesTrajectory"), sample.length, grid = 1e-05, plot.prediction = TRUE) ```

## Arguments

 `object` class object of MCMC samples: "est.NHPP", created with method `estimate,NHPP-method` `variable` if prediction of event times ("eventTimes") or of Poisson process variables ("PoissonProcess") `t` vector of time points to make predictions for (only for variable = "PoissonProcess") `burnIn` burn-in period `thinning` thinning rate `Lambda.mat` matrix-wise definition of drift function (makes it faster) `which.series` which series to be predicted, new one ("new") or further development of current one ("current") `Tstart` optional, if missing, first (which.series = "new") or last observation variable ("current") is taken `M2pred` optional, if current series to be predicted and t missing, `M2pred` variables will be predicted with the observation time distances `rangeN` vector of candidate area for differences of N, only if pred.alg = "Distribution" and variable = "PoissonProcess" `cand.length` length of candidate samples (if method = "vector") `pred.alg` prediction algorithm, "Distribution", "Trajectory", "simpleTrajectory" or "simpleBayesTrajectory" `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 14 15 16 17 18 19 20 21 22 23 24``` ```model <- set.to.class("NHPP", parameter = list(xi = c(5, 1/2)), Lambda = function(t, xi) (t/xi[2])^xi[1]) t <- seq(0, 1, by = 0.01) data <- simulate(model, t = t) est <- estimate(model, t, data\$Times, 1000) # nMCMC should be much larger! plot(est) pred <- predict(est, Lambda.mat = function(t, xi) (t/xi[,2])^xi[,1], variable = "PoissonProcess", pred.alg = "Distribution") ## Not run: pred_NHPP <- predict(est, Lambda.mat = function(t, xi) (t/xi[,2])^xi[,1]) pred_NHPP <- predict(est, variable = "PoissonProcess", Lambda.mat = function(t, xi) (t/xi[,2])^xi[,1]) pred_NHPP2 <- predict(est, which.series = "current", Lambda.mat = function(t, xi) (t/xi[,2])^xi[,1]) pred_NHPP3 <- predict(est, variable = "PoissonProcess", which.series = "current", Lambda.mat = function(t, xi) (t/xi[,2])^xi[,1]) pred_NHPP4 <- predict(est, pred.alg = "simpleTrajectory", M2pred = length(data\$Times)) ## End(Not run) pred_NHPP <- predict(est, variable = "PoissonProcess", pred.alg = "simpleTrajectory", M2pred = length(data\$Times)) pred_NHPP <- predict(est, variable = "PoissonProcess", pred.alg = "simpleBayesTrajectory", M2pred = length(data\$Times), sample.length = 100) ```

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