Simulate the trajectory of a Gaussian process, given a mean vector and a variance-covariance structure.

sim_gaussianProcess( mean_vec, varCov_mat, from = 0, to = 1, start = 0, nb.points = 10 )

mean_vec | Vector (if multimensional) of means for the increments following gaussian distribution. |
---|---|

varCov_mat | Corresponding variance-covariance structure. |

from | Initial time point at which the process is simulated. |

to | Last time point at which the process is simulated. |

start | Useful if the user wants to make the trajectory start from some given value. |

nb.points | Number of points at which the process is simulated. |

The trajectory of the Gaussian processes after simulating the multivariate Gaussian distributions with specified variance-covariance structure.

Xavier Milhaud xavier.milhaud.research@gmail.com

list.comp <- list(f1 = "norm", g1 = "norm") list.param <- list(f1 = list(mean = 12, sd = 0.4), g1 = list(mean = 16, sd = 0.7)) sample1 <- rsimmix(n = 2000, unknownComp_weight = 0.5, comp.dist = list.comp, comp.param = list.param)$mixt.data ## First get the variance-covariance matrix of the empirical process (Donsker correlation): cov_mat <- .Call('_admix_estimVarCov_empProcess_Rcpp', PACKAGE = 'admix', seq(from = min(sample1), to = max(sample1), length.out = 100), sample1) ## Plug it into the simulation of the gaussian process: B1 <- sim_gaussianProcess(mean_vec=rep(0,nrow(cov_mat)), varCov_mat=cov_mat, from=min(sample1), to = max(sample1), start = 0, nb.points = nrow(cov_mat)) plot(x = B1$dates, y = B1$traj1, type="l", xlim = c(min(sample1),max(sample1)), ylim = c(-1,1))