setPPR | R Documentation |
Constructor of a Point Process Regression Model
setPPR(yuima, counting.var = "N", gFun, Kernel, var.dx = "s", var.dt = "s", lambda.var = "lambda", lower.var = "0", upper.var = "t", nrow = 1, ncol = 1)
yuima |
an object of |
counting.var |
a label denoting the name of the counting process. |
gFun |
a vector string that is the mathematical expression of the vector function |
Kernel |
a matrix string that is the kernel |
var.dx |
a string denoting the integration variable in the intensity process. |
var.dt |
a string denoting the integration time variable in the intensity process. |
lambda.var |
name of the intensity process. |
lower.var |
Lower bound of the support for the integral in the definition of the intensity process. |
upper.var |
Upper bound of the support for the integral in the definition of the intensity process. |
nrow |
number of rows in the kernel. |
ncol |
number of columns in the kernel. |
An object of yuima.PPR
There may be missing information in the model description. Please contribute with suggestions and fixings.
The YUIMA Project Team
Contacts: Lorenzo Mercuri lorenzo.mercuri@unimi.it
Insert Here References
## Not run: ## Hawkes process with power law kernel # I. Law Definition: my.rHwk2 <- function(n){ as.matrix(rep(1,n)) } Law.Hwk2 <- setLaw(rng = my.rHwk2, dim = 1) # II. Definition of the counting process N_t mod.Hwk2 <- setModel(drift = c("0"), diffusion = matrix("0",1,1), jump.coeff = matrix(c("1"),1,1), measure = list(df = Law.Hwk2), measure.type = "code", solve.variable = c("N"), xinit=c("0")) # III. Definition of g() and kappa() g.Hwk2 <- "mu" Kern.Hwk2 <- "alpha/(1+(t-s))^beta" # IV. Construction of an yuima.PPR object PPR.Hwk2 <- setPPR(yuima = mod.Hwk2, gFun=g.Hwk2, Kernel = as.matrix(Kern.Hwk2),var.dx = "N") ## End(Not run)
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