predSDE: Bayesian prediction in mixed SDE models

In SimoneHermann/hierRegSDE:

Description

Bayesian prediction in the mixed SDE dY_j(t)= b(φ_j, t, Y_j(t))dt + γ s(t, Y_j(t)) dW_j(t), , φ_j~N(μ, Ω).

Usage

predSDE(tau, samples, last, bSDE, sVar, cand, mod = "Gompertz", modVar = "")

Arguments

tau	vector of times which are predicted
samples	list of samples from the posterior
last	last observation - staring point for the prediction of Markov chain
bSDE	drift function
sVar	variance function
cand	vector of candidates for trajection sampling
mod	model out of Gompertz, Richards, logistic, Weibull, only used instead of bSDE
modVar	model for the variance structure

Value

matrix of predictions in t

Examples

mod <- "Gompertz"
bSDE <- getFun("SDE", mod)
mu <- getPar("SDE", mod, "truePar")
n <- 5
parameters <- defaultPar(mu, n)
y <- drawData("SDE", bSDE, parameters)
t <- parameters$t

prior <- getPrior(mu, parameters$gamma2)
start <- getStart(mu, n)
chains <- estSDE(t, y, prior, cut = 50, ipred = 1, start, bSDE, len = 5000)
ind <- seq(1001, 5000, by = 4)
samples <- list(phi = sapply(1:length(mu), function(i) phi = phi_ij(chains$phi, 1, i))[ind, ], gamma2 = chains$gamma2[ind])
prediction <- predSDE(t[50:101], samples, y[1,50], bSDE, cand = seq(-2, 2, length = 1000))
plot(t[51:101], y[1,51:101], ylim = range(y[1,51:101]) + c(0, 1))
lines(t[51:101], apply(prediction, 1, quantile, 0.025), col = 3)
lines(t[51:101], apply(prediction, 1, quantile, 0.975), col = 3)
lines(t[51:101], apply(prediction, 1, mean), col = 2)

SimoneHermann/hierRegSDE documentation built on May 9, 2019, 1:46 p.m.