genLatentY: Generation of data from the copula-based Markov model of...

In CopCTS: Copula-Based Semiparametric Analysis for Time Series Data with Detection Limits

Description

Generate the latent response variable from the assumed copula-based Markov model in Li, Tang and Wang (2018).

Usage

genLatentY(cop="Gaussian",theta,N,MARGIN.inv=qnorm,...)

Arguments

cop	the choice of copula function. There are currently five available copula funcitons, including Clayton copula, Gaussian copula, Gumbel copula, Joe copula and Frank copula. Specify one from "Clayton","Gaussian","Gumbel","Joe" and "Frank". The default is "Gaussian".
theta	the copula parameter.
N	the length of the latent response.
MARGIN.inv	the inverse marginal distribution function of the latent time series. The default is qnorm(p,mean=0,sd=1), i.e., the standard normal marginal.
...	additional parameters for the inverse marginal distribution funcion of the latent time series.

Value

genLatentY returns a Nx1 vector of the latent response variable Y*

References

Li, F., Tang, Y. and Wang, H. (2018) Copula-based Semiparametric Analysis for Time Series Data with Detection Limits, technical report.

CopCTS documentation built on May 1, 2019, 8:18 p.m.