gammacap_adf: Asymptotic Distribution-Free Covariance Matrix

In jeksterslab/gammaMatrix: Asymptotic Covariance Matrix of Covariances

Description

Calculates the asymptotic distribution-free (ADF) covariance matrix of the unique elements of the covariance matrix.

Usage

gammacap_adf(x, unbiased = TRUE, names = TRUE, sep = ".")

gammacap_adfunbiased(gammacaptilde, sigmacaptilde, n)

Arguments

x	Numeric matrix, data frame, or vector.
unbiased	Logical. If unbiased = TRUE, returns unbiased asymptotic distribution-free covariance matrix. If unbiased = FALSE, returns consistent asymptotic distribution-free covariance matrix.
names	Logical. Add names.
sep	Character string. Separator for variable names.
gammacaptilde	Numeric matrix. Consistent estimate of the asymptotic distribution-free covariance matrix.
sigmacaptilde	Numeric matrix. Consistent estimate of the sample covariance matrix.
n	Positive integer. Sample size.

Value

A matrix.

Dependencies

• rmvn_chol() (test)

Author(s)

Ivan Jacob Agaloos Pesigan

References

Browne, M. W. (1984). Asymptotically distribution-free methods for the analysis of covariance structures. British Journal of Mathematical and Statistical Psychology, 37, 62–83. doi:10.1111/j.2044-8317.1984.tb00789.x.

See Also

Other Gamma Matrix Functions: gammacap_adfnb(), gammacap_gen(), gammacap_mvnadj1(), gammacap_mvnadj2(), 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_adf(x, unbiased = TRUE)
gammacap_adf(x, unbiased = FALSE)

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