vcov.lmvar: Variance-covarience matrix of the coefficients beta for an...

In lmvar: Linear Regression with Non-Constant Variances

Description

Variance-covarience matrix (also simply called the 'covariance matrix') for the maximum-likelihood estimators of β_μ and β_σ. The matrix is calculated with the assumption of asymptotic normality of maximum likelihood estimators. This assumption is only valid in the limit of a large number of observations.

Usage

## S3 method for class 'lmvar'
vcov(object, mu = TRUE, sigma = TRUE, ...)

Arguments

object	Object of class 'lmvar'
mu	Specifies whether or not the covariance matrix for β_μ is included in the returned matrix
sigma	Specifies whether or not the covariance matrix for β_σ is included in the returned matrix
...	For compatibility with vcov generic

Details

The variance-covariance matrix is calculated as I^{-1} / n where I is the Fisher information matrix and n the number of observations.

When mu = TRUE and sigma = TRUE, the full covariance matrix for the combined vector (β_μ, β_σ) is returned.

When mu = TRUE and sigma = FALSE, only the covariance matrix for β_μ is returned.

When mu = FALSE and sigma = TRUE, only the covariance matrix for β_σ is returned.

Value

A 'matrix' object containing the (approximate) variance-covariance matrix of the maximum-likelihood estimators of β_μ and β_σ in object.

See Also

summary.lmvar for standard errors for β_μ and β_μ.

nobs.lmvar_no_fit for the number of observations in an object of class 'lmvar'.

fisher for the Fisher information matrix of an object of class 'lmvar'.

See the vignette "Math" (to be viewed with vignette("Math", "lmvar")) for details.

lmvar documentation built on May 16, 2019, 5:06 p.m.