varcov {bqtl} R Documentation

## Create moment matrices

### Description

Create a moment matrix of the marker variables and of the regressors by the phenotype variable. For use in regression modelling on the markers.

### Usage

```varcov(x, ana.obj, partial=NULL, scope,...)
```

### Arguments

 `x` A formula to specify the dependent and independent variables to be used in subsequent calculations e.g `trait ~ locus(.) ` `ana.obj` An `analysis.object`, see`make.analysis.obj` `partial` A formula whose right hand side specifies variables to be treated as covariates. `scope` Usually not explicitly used. Optional vector of variable names. `...` ignored

### Details

This is just a wrapper for `make.varcov`.

### Value

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` The degrees of freedom, when no variables are specified in `partial` it is ` sum(subset==TRUE) - 1`

### Note

It is generally NOT a good idea to do regressions on ill-conditioned designs using the moment matrices. 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 ill-conditioning. Of course the rational for using the method is to speed the sampling, and it is very effective at doing so.

### Author(s)

Charles C. Berry cberry@ucsd.edu

The examples in `swap` and `twohk`.