gammacap_mvnadj2: Asymptotic Covariance Matrix with Adjustment (Variant 2)

In jeksterslab/gammaMatrix: Asymptotic Covariance Matrix of Covariances

Description

Calculates the covariance matrix of the unique elements of the covariance matrix with adjustment for nonnormality.

Usage

gammacap_mvnadj2(
  x,
  missing = FALSE,
  ml_cov = TRUE,
  drop_means = TRUE,
  names = TRUE,
  sep = "."
)

Arguments

x	Numeric matrix, data frame, or vector.
missing	Logical. If missing = TRUE, the mean vector and the covariance matrix will be estimated using the EM algorithm. If missing = FALSE, all missing values will be dropped and the mean vector and covariance matrix will be estimated using x.
ml_cov	Logical. If missing = FALSE and ml_cov = TRUE, use maximum likelihood estimator of the covariance matrix.
drop_means	Logical. If drop_means = TRUE, remove the means from the output matrix. If drop_means = FALSE, the first ncol(x) elements of the output matrix will be names for the ncol(x) means.
names	Logical. Add names.
sep	Character string. Separator for variable names.

Value

A matrix.

Dependencies

• rmvn_chol() (test)

Author(s)

Ivan Jacob Agaloos Pesigan

References

Add appropriate references here...

See Also

Other Gamma Matrix Functions: gammacap_adfnb(), gammacap_adf(), gammacap_gen(), gammacap_mvnadj1(), gammacap_mvn(), 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_mvnadj2(x)
gammacap_mvnadj2(x, drop_means = FALSE)

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