vcov.madness {madness} | R Documentation |
Calculate Variance-Covariance Matrix for a model.
Description
Returns the variance-covariance matrix of the parameters
computed by a madness
object.
Usage
## S3 method for class 'madness'
vcov(object, ...)
Arguments
object |
a |
... |
additional arguments for method functions. Ignored here. |
Details
Let X
represent some quantity which is estimated from
data. Let \Sigma
be the (known or estimated)
variance-covariance matrix of X
. If Y
is some computed function of X
, then, by the
Delta method (which is a first order Taylor approximation),
the variance-covariance matrix of Y
is approximately
\frac{\mathrm{d}Y}{\mathrm{d}{X}} \Sigma \left(\frac{\mathrm{d}Y}{\mathrm{d}{X}}\right)^{\top},
where the derivatives are defined over the 'unrolled' (or vectorized)
Y
and X
.
Note that Y
can represent a multidimensional quantity. Its
variance covariance matrix, however, is two dimensional, as it too
is defined over the 'unrolled' Y
.
Value
A matrix of the estimated covariances between the values being
estimated by the madness
object. While Y
may be
multidimensional, the return value is a square matrix whose side length
is the number of elements of Y
Author(s)
Steven E. Pav shabbychef@gmail.com
See Also
vcov
.
Examples
y <- array(rnorm(2*3),dim=c(2,3))
dy <- matrix(rnorm(length(y)*2),ncol=2)
dx <- crossprod(matrix(rnorm(ncol(dy)*100),nrow=100))
obj <- madness(val=y,dvdx=dy,varx=dx)
print(vcov(obj))