> On Tue, 12 Sep 2000, Magill, Brett wrote:
>
> > Does anyone know of code to conduct hierarchical (that is,
multi-level)
> > models using R. Beyond simply requiring a nested design, I want to
model
> > explicitly the covariance between levels as is done in such
multi-level
> > modeling software as HLM or MLwin and discussed in Goldestein (1999)
>
> The lme() function in the nlme package will fit most of these linear
> hierarchical models.
>
> There is code in Jim Lindsey's 'repeated' package (his page is
linked from
> CRAN) for generalised linear models with random intercepts, but not, I
> think, for more general hierarchical structures.
In growth, carma does linear models with random coefficients and
elliptic does 2- and 3-level hierarchical models (random intercepts)
both also with the possibility of AR. Both use normal distribution
with normal random effects; elliptic also has elliptically-contoured
distributions (Student t and power exponential) and allows nonlinear
models.
In repeated, kalseries does linear and nonlinear models with a wide
variety of distributions including normal, with gamma random effect
and/or serial correlations. glmm and gnlmm do generalized linear and
nonlinear models respectively with normal random intercept.
Jim
>
> -thomas
>
> Thomas Lumley
> Assistant Professor, Biostatistics
> University of Washington, Seattle
>
>
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