[R] lme parameterization question
spencer.graves at pdf.com
Thu Apr 3 13:39:09 CEST 2003
Have you looked at Pinheiro and Bates (2000) Mixed Effects Models in S
and s-Plus? "lme" is great, but I couldn't make it work until I spent
some time with that book.
Hope this helps.
John Fieberg wrote:
> I am trying to parameterize the following mixed model (following Piepho
> and Ogutu 2002), to test for a trend over time, using multiple sites:
> y[ij]=mu+b[j]+a[i]+w[j]*(beta +t[i])+c[ij]
> y[ij]= a response variable at site i and year j
> mu = fixed intercept
> Beta=fixed slope
> w[j]=constant representing the jth year (covariate)
> b[j]=random effect of jth year, iid N(0,sigma2[b])
> a[i]=random effect of the ith site, iid N(0, sigma2[a])
> t[i]=random effect of ith site, iid N(0, sigma2[t])
> c[ij]=random error associated with ith site and jth year
> I would like to assume that an unstructured relationship applies to
> a[i] and t[i] (i.e., I would like to assume that the random effects a[i]
> and t[i] are drawn from a multivariate normal distribution with non-zero
> covariance parameter). These random effects are assumed to be
> independent from the b[j]'s and from the c[ij]'s. I have tried several
> approaches, but cannot seem to duplicate the results presented in Piepho
> and Ogutu using R's lme function (but I can reproduce the results using
> SAS proc mixed).
> In SAS, the model is fit using:
> proc mixed method=REML nobound;
> class year site;
> model y=w site/ddfm=satterth s;
> random int/sub=year;
> random int w/sub=site type=un;
> Any help would be greatly appreciated!
> Piepho, H-P. and J.O.Ogutu. 2002. A simple mixed model for trend
> analysis in wildlife populations. Journal of Agricultural, Biological,
> and Environmental Statistics, 7(3):350-360.
> R-help at stat.math.ethz.ch mailing list
More information about the R-help