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#--- lotka-volterra model --------------------------------------------------
#library(msde)
context("lotka-volterra model -- sd scale (precompiled)")
# setup lotvol model
## ModelFile <- "hestModel.h"
## param.names <- c("alpha", "gamma", "beta", "sigma", "rho")
## data.names <- c("X", "Z")
## model <- sde.make.model(ModelFile = ModelFile,
## param.names = param.names,
## data.names = data.names)
model <- sde.examples(model = "lotvol")
# test parameters
test.params <- list(dT.max = 1, dT.pf = .1,
test.pf = TRUE)
# lotka-volterra model drift and diffusion
drift.fun <- function(x, theta) {
if(!is.matrix(x)) x <- t(x)
if(!is.matrix(theta)) theta <- t(theta)
dr <- cbind(theta[,1]*x[,1] - theta[,2]*x[,1]*x[,2], # alpha * H - beta * H*L
theta[,2]*x[,1]*x[,2] - theta[,3]*x[,2]) # beta * H*L - gamma * L
dr
}
diff.fun <- function(x, theta) {
if(!is.matrix(x)) x <- t(x)
if(!is.matrix(theta)) theta <- t(theta)
df <- matrix(NA, nrow(x), 4)
df[,1] <- theta[,1]*x[,1] + theta[,2]*x[,1]*x[,2] # alpha * H + beta * H*L
df[,2] <- -theta[,2]*x[,1]*x[,2] # -beta * H*L
df[,3] <- df[,2] # -beta * H*L
df[,4] <- theta[,2]*x[,1]*x[,2] + theta[,3]*x[,2] # beta * H*L + gamma * L
t(apply(df, 1,
function(xx) chol(matrix(xx,2,2)))) # always use sd scale in R
}
# generate heston data/parameters
randx <- function(nreps) {
X0 <- c(H = 71, L = 79)
if(nreps > 1) X0 <- apply(t(replicate(nreps, X0)), 2, jitter)
X0
}
randt <- function(nreps) {
Theta <- c(alpha = .5, beta = .01, gamma = .3)
if(nreps > 1) Theta <- apply(t(replicate(nreps, Theta)), 2, jitter)
Theta
}
validx <- function(x, theta) {
all(x > 0)
}
source("msde-test_debug.R", local = TRUE)
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