aeDensity | R Documentation |
Asymptotic Expansion - Density
aeDensity(..., ae, eps = 1, order = NULL)
... |
named argument, data.frame, list, or environment specifying the grid to evaluate the density. See examples. |
ae |
an object of class |
eps |
numeric. The intensity of the perturbation. |
order |
integer. The expansion order. If |
Probability density function evaluated on the given grid.
## Not run: # model gbm <- setModel(drift = 'mu*x', diffusion = 'sigma*x', solve.variable = 'x') # settings xinit <- 100 par <- list(mu = 0.01, sigma = 0.2) sampling <- setSampling(Initial = 0, Terminal = 1, n = 1000) # asymptotic expansion approx <- ae(model = gbm, sampling = sampling, order = 4, true.parameter = par, xinit = xinit) # The following are all equivalent methods to specify the grid via .... # Notice that the character 'x' corresponds to the solve.variable of the yuima model. # 1) named argument x <- seq(50, 200, by = 0.1) density <- aeDensity(x = x, ae = approx, order = 4) # 2) data frame df <- data.frame(x = seq(50, 200, by = 0.1)) density <- aeDensity(df, ae = approx, order = 4) # 3) environment env <- new.env() env$x <- seq(50, 200, by = 0.1) density <- aeDensity(env, ae = approx, order = 4) # 4) list lst <- list(x = seq(50, 200, by = 0.1)) density <- aeDensity(lst, ae = approx, order = 4) # exact density exact <- dlnorm(x = x, meanlog = log(xinit)+(par$mu-0.5*par$sigma^2)*1, sdlog = par$sigma*sqrt(1)) # compare plot(x = exact, y = density, xlab = "Exact", ylab = "Approximated") ## End(Not run)
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