vcov.rzlm {complexlm}

Calculate Variance-Covariance Matrix and Pseudo Variance-Covariance Matrix for a Complex Fitted Model Object

Description

A version of stats::vcov that is compatible with complex linear models. In addition to the variance-covariance matrix, the pseudo variance-covariance matrix, which is a measure of the covariance between real and imaginary components, is returned as well. Can also return the "big covariance" matrix, which combines the information of the covariance matrix and the pseudo-covariance matrix, as described in (van den Bos 1995). While not as compact as two seperate smaller matrices, the big covariance matrix simplifies calculations such as the mahalanobis distance.

Usage

## S3 method for class 'rzlm'
vcov(object, merge = TRUE, ...)

Arguments

object

a fitted model object, typically. Sometimes also a summary() object of such a fitted model.

merge

logical. Should the covariance matrix and pseudo-covariance / relational matrix be merged into one matrix of twice the dimensions? Default is TRUE.

...

Additional parameters, not currently used for anything.

Value

If merge is false, a list containing both the numeric variance-covariance matrix, and the complex pseudo variance-covariance matrix. If merge is true, a large matrix (both dimensions twice the number of coefficients) containing both the variance-covariance matrix and the pseudo variance-covariance matrix, merged together.

References

A. van den Bos, The Multivariate Complex Normal Distribution-a Generalization, IEEE Trans. Inform. Theory 41, 537 (1995).

Examples

set.seed(4242)
n <- 8
slope <- complex(real = 4.23, imaginary = 2.323)
intercept <- complex(real = 1.4, imaginary = 1.804)
e <- complex(real=rnorm(n)/6, imaginary=rnorm(n)/6)
x <- complex(real = rnorm(n), imaginary = rnorm(n))
y <- slope * x + intercept + e
robfit <- rlm(x = x, y = y, weights = rep(1,n), interc = TRUE)
summary(robfit)$stddev
vcov(robfit)