Description Usage Arguments References Examples
Bayesian prediction of a non-homogeneous Poisson process with cumulative intensity function Λ(t, ξ).
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)
|
object |
class object of MCMC samples: "est.NHPP", created with 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, |
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 |
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 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)
|
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