covsim (version 1.1.0)

vita: Calibrate a regular vine

Description

vita implements the VITA (VIne-To-Anything) algorithm. Covariance matrix and margins are specified, and vita calibrates the pair-copulas in each node of the tree to match the target covariance.

Usage

vita(
  margins,
  sigma.target,
  vc = NULL,
  family_set = c("clayton", "gauss", "joe", "gumbel", "frank"),
  Nmax = 10^6,
  numrootpoints = 10,
  conflevel = 0.995,
  numpoints = 4,
  verbose = TRUE,
  cores = parallel::detectCores()
)

Value

If a feasible solution was found, a vine to be used for simulation

Arguments

margins

A list where each element corresponds to a margin. Each margin element is a list containing the distribution family ("distr") and additional parameters. Must be a distribution available in the stats package.

sigma.target

The target covariance matrix that is to be matched. The diagonal elements must contain the variances of marginal distributions.

vc

A vine dist object as specified by the rvinecopulib package. This object specifies the vine that is to be calibrated. If not provided, a D-vine is assumed.

family_set

A vector of one-parameter pair-copula families that is to be calibrated at each node in the vine. Possible entries are "gauss", "clayton", "joe", "gumbel" and "frank". Calibration of pair-copula families is attempted in the order provided.

Nmax

The sample size used for calibration. Reduce for faster calibration, at the cost of precision.

numrootpoints

The number of estimated roots at the initial calibration stage, which determines a search interval where Nmax samples are drawn

conflevel

Confidence level for determining search interval

numpoints

The number of samples drawn with size Nmax, to determine the root within search interval To increase precision increase this number. To calibrate faster (but less precisely), may be reduced to a number no lower than 2

verbose

If TRUE, outputs details of calibration of each bicopula

cores

Number of cores to use. If larger than 1, computations are done in parallel. May be determined with parallel:detectCores()

References

Grønneberg, S., Foldnes, N., & Marcoulides, K. M. (2021). covsim: An r package for simulating non-normal data for structural equation models using copulas. Journal of Statistical Software. doi:10.18637/jss.v102.i03

Examples

set.seed(1)# define a target covariance. 3 dimensions.
sigma.target <- cov(MASS::mvrnorm(10, mu=rep(0,3), Sigma=diag(1, 3)))

#normal margins that match the covariances:
marginsnorm <- lapply(X=sqrt(diag(sigma.target)),function(X) list(distr="norm", sd=X) )

#calibrate with a default D-vine, with rather low precision (default Nmax is 10^6)
# if cores=1 is removed, all cores are used, with a speed gain
calibrated.vine <- vita(marginsnorm, sigma.target =sigma.target, Nmax=10^5, cores=1)
#check
#round(cov(rvinecopulib::rvine(10^5, calibrated.vine))-sigma.target, 3)

#margins are normal but dependence structure is not
#pairs(rvinecopulib::rvine(500, calibrated.vine))