gammacap_mvn: Asymptotic Covariance Matrix Assuming Multivariate Normal...

In jeksterslab/gammaMatrix: Asymptotic Covariance Matrix of Covariances

Description

Calculates the covariance matrix of the unique elements of the covariance matrix assuming multivariate normal data.

Usage

gammacap_mvn(x, sigmacap = NULL, names = TRUE, sep = ".")

Arguments

x	Numeric matrix, data frame, or vector.
sigmacap	Numeric matrix. Optional argument. Sample covariance matrix.
names	Logical. Add names.
sep	Character string. Separator for variable names.

Value

A matrix.

Dependencies

• dcap()

• kcap()

• rmvn_chol() (test)

Author(s)

Ivan Jacob Agaloos Pesigan

References

Browne, M. W., & Arminger, G. (1995). Specification and estimation of mean-and covariance-structure models. Handbook of statistical modeling forthe social and behavioral sciences.

See Also

Other Gamma Matrix Functions: gammacap_adfnb(), gammacap_adf(), gammacap_gen(), gammacap_mvnadj1(), gammacap_mvnadj2(), gammacap_nb(), gammacap_ols_generic(), gammacap_ols_hc_generic(), gammacap_ols_hc_qcap_generic(), gammacap_ols_hc_qcap(), gammacap_ols_hc(), gammacap_ols(), gammacapnames(), gammacap()

Examples

set.seed(42)
n <- 1000
k <- 2
z <- matrix(
  data = rnorm(n = n * k), nrow = n, ncol = k
)
q <- chol(
  matrix(
    data = c(1.0, 0.5, 0.5, 1.0),
    nrow = k, ncol = k
  )
)
x <- z %*% q

gammacap_mvn(x)
gammacap_mvn(sigmacap = cov(x))

jeksterslab/gammaMatrix documentation built on Dec. 20, 2021, 10:10 p.m.