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

## 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

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 9, 2019, 1:46 p.m.