need help with mixed effects model
Hi Nick, You might want to look at the sem package. Hank
On Mar 5, 2008, at 6:02 AM, Nick Isaac wrote:
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 at 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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