yuima (version 1.1.6)

yuima.multimodel-class: Class for the mathematical description of Multi dimensional Jump Diffusion processes

Description

The yuima.multimodel class is a class of the yuima package that extends the yuima.model-class.

Slots

Methods

Examples

## Not run: 
# # We define the density function of the underlying Levy
# 
# dmyexp <- function(z, sig1, sig2, sig3){
#   rep(0,3)
# }
# 
# # We define the random number generator
# 
# rmyexp <- function(z, sig1, sig2, sig3){
#   cbind(rnorm(z,0,sig1), rgamma(z,1,sig2), rnorm(z,0,sig3))
# }
# 
# # Model Definition: in this case we consider only a multi
# # compound poisson process with a common intensity as underlying
# # noise
# 
# mod <- setModel(drift = matrix(c("0","0","0"),3,1), diffusion = NULL,
#   jump.coeff = matrix(c("1","0","0","0","1","-1","1","0","0"),3,3),
#   measure = list( intensity = "lambda1", df = "dmyexp(z,sig1,sig2,sig3)"),
#   jump.variable = c("z"), measure.type=c("CP"),
#   solve.variable=c("X1","X2","X3"))
# 
# # Sample scheme
# 
# samp<-setSampling(0,100,n=1000)
# param <- list(lambda1 = 1, sig1 = 0.1, sig2 = 0.1, sig3 = 0.1)
# 
# # Simulation
# 
# traj <- simulate(object = mod, sampling = samp,
#   true.parameter = param)
# 
# # Plot
# 
# plot(traj, main = " driven noise. Multidimensional CP",
#   cex.main = 0.8)
# 
# # We construct a multidimensional SDE driven by a multivariate
# # levy process without CP components.
# 
# # Definition multivariate density
# 
# dmyexp1 <- function(z, sig1, sig2, sig3){
#   rep(0,3)
# }
# 
# # Definition of random number generator
# # In this case user must define the delta parameter in order to
# # control the effect of time interval in the simulation.
# 
# rmyexp1 <- function(z, sig1, sig2, sig3, delta){
#   cbind(rexp(z,sig1*delta), rgamma(z,1*delta,sig2), rexp(z,sig3*delta))
# }
# 
# # Model defintion
# 
# mod1 <- setModel(drift=matrix(c("0.1*(0.01-X1)",
#   "0.05*(1-X2)","0.1*(0.1-X3)"),3,1), diffusion=NULL,
#    jump.coeff = matrix(c("0.01","0","0","0","0.01",
#                           "0","0","0","0.01"),3,3),
#    measure = list(df="dmyexp1(z,sig1,sig2,sig3)"),
#    jump.variable = c("z"), measure.type=c("code"),
#    solve.variable=c("X1","X2","X3"),xinit=c("10","1.2","10"))
# 
# # Simulation sample paths
# 
# samp<-setSampling(0,100,n=1000)
# param <- list(sig1 = 1, sig2 = 1, sig3 = 1)
# 
# # Simulation
# 
# set.seed(1)
# traj1 <- simulate(object = mod1, sampling = samp,
#   true.parameter = param)
# 
# # Plot
# 
# plot(traj1, main = "driven noise: multi Levy without CP",
#   cex.main = 0.8)
# 
# # We construct a multidimensional SDE driven by a multivariate
# # levy process.
# 
# # We consider a mixed situation where some
# # noise are driven by a multivariate Compuond Poisson that
# # shares a common intensity parameters.
# 
# ### Multi Levy model
# 
# rmyexample2<-function(z,sig1,sig2,sig3, delta){
#     if(missing(delta)){
#       delta<-1
#     }
#     cbind(rexp(z,sig1*delta), rgamma(z,1*delta,sig2),
#         rexp(z,sig3*delta), rep(1,z),
#         rep(1,z))
# }
# 
# dmyexample2<-function(z,sig1,sig2,sig3){
#   rep(0,5)
# }
# 
# # Definition Model
# 
# mod2 <- setModel(drift=matrix(c("0.1*(0.01-X1)",
#   "0.05*(1-X2)","0.1*(0.1-X3)", "0", "0"),5,1), diffusion=NULL,
#   jump.coeff = matrix(c("0.01","0","0","0","0",
#                         "0","0.01","0","0","0",
#                         "0","0","0.01","0","0",
#                         "0","0","0","0.01","0",
#                         "0","0","0","0","0.01"),5,5),
#   measure = list(df = "dmyexample2(z,sig1,sig2,sig3)",
#             intensity = "lambda1"),
#   jump.variable = c("z"),
#   measure.type=c("code","code","code","CP","CP"),
#   solve.variable=c("X1","X2","X3","X4","X5"),
#   xinit=c("10","1.2","10","0","0"))
# 
# # Simulation scheme
# samp <- setSampling(0, 100, n = 1000)
# param <- list(sig1 = 1, sig2 = 1, sig3 = 1, lambda1 = 1)
# 
# # Simulation
# 
# set.seed(1)
# traj2 <- simulate(object = mod2, sampling = samp,
#   true.parameter = param)
# 
# plot(traj2, main = "driven noise: general multi Levy", cex.main = 0.8)
# 
# ## End(Not run)