You can do SEM-type models using the smatr package. Only drawback is that
you'd have to treat Rat as a fixed effect. This is a general class of
problem that afflicts several of my current projects, and I'm having a tough
time choosing between mixed effect and structural equation models. The
former is most appropriate for partitioning the variance, but the latter is
most appropriate for modelling error variance in the observations. I don't
see an obvious solution with the available tools and would appreciate any
general insights.

Cheers, Nick


On 04/03/2008, Ken Beath <kjbeath@kagi.com> wrote:
>
> The concern that Doug had is I assume that gene1 and gene2 are both
> measured with error, and this type of model assumes that the
> covariates are measured without error or for practical purposes much
> lower than the error in the dependent variable. Ignoring this problem
> biases the coefficients towards zero with consequent loss of power. I
> don't have any idea how important this is, it all depends on the error
> of your measurements. The usual solution is structural equation
> modelling (SEM). This is something I haven't tried, so I have no idea
> how easy or how well it will work.
>
>

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