make.varcov  R Documentation 
Create a moment matrix of the marker variables and of the regressors by the phenotype variable. For use in regression modelling on the markers.
make.varcov(regressor.matrix, y, subset=is.finite(y), casewt=NULL)
regressor.matrix 
The object produced by 
y 
A vector of phenotype information with the same number of
elements as there are rows in 
subset 
Logical vector with the same number of
elements as there are rows in 
casewt 
Optional vector of case weights. 
A list with components
var.x 
Moment matrix of the marker regressor variables 
cov.xy 
Moment matrix of the marker regressor variables versus the phenotype variable 
var.y 
The Second central moment of the phenotype variable 
df 

It is generally NOT a good idea to do regressions on
illconditioned designs using the moment matrices like this. The
excuse for doing so here is twofold. First, calculations using this
method are used to perform importance sampling, so minor numerical
inaccuracies in computing the probabilites used in sampling get
straightened out by the importance weights. Second, it will typically
be the case that a prior is set on the regression coefficients and
this results in a positive constant (aka a 'ridge' parameter) being
added to diagonal of varcov$var.x
and this reduces the
illconditioning. Of course the rational for using the method is to
speed the sampling, and it is very effective at doing so.
Charles C. Berry cberry@ucsd.edu
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