Different within-group error correlation matrices per group

Dear all,

I am working with lme and I would like to have different within-group error
correlation matrices per group (\Lambda_i in 5.1 from Pinheiro & Bates).

Currently, my sentence looks like the following

correlation = corCompSymm(form = ~ 1 | ind)


which imposes the SC structure into the within-group error correlation
matrix, marking the individuals by "ind".

According to the following example in SAS given by an instructor, it is
possible to estimate different within-group error correlation matrices per
sex using the following sentences (see "group=gender" in fourth line):

proc mixed  data=dental;

  class  child gender;
  model distance = gender gender*age / noint solution;
  repeated / group=gender subject=child;
  random intercept age / type=un subject=child g gcorr v vcorr;
run;

I cannot figure out how to reproduce this in R. I took a look at the
documentation as well as the first five chapters of Pinheiro & Bates (2000)
without luck.

Cheers!

        [[alternative HTML version deleted]]

lme(fixed = y~1+time, random = ~1+time|ind, correlation = NULL, weights =

You might find the following links contain some useful information.


http://stackoverflow.com/questions/11819720/converting-repeated-measures-mixed-model-formula-from-sas-to-r

Factor-specific variances in R
https://rpubs.com/bbolker/6298

Harris wateruse
http://www.inside-r.org/packages/cran/agridat/docs/harris.wateruse


On Tue, Oct 27, 2015 at 8:57 PM, Luis Damiano <luiggi at gmail.com> wrote:

Dear all,

I am working with lme and I would like to have different within-group
error
correlation matrices per group (\Lambda_i in 5.1 from Pinheiro & Bates).

Currently, my sentence looks like the following

correlation = corCompSymm(form = ~ 1 | ind)


which imposes the SC structure into the within-group error correlation
matrix, marking the individuals by "ind".

According to the following example in SAS given by an instructor, it is
possible to estimate different within-group error correlation matrices per
sex using the following sentences (see "group=gender" in fourth line):

proc mixed  data=dental;

  class  child gender;
  model distance = gender gender*age / noint solution;
  repeated / group=gender subject=child;
  random intercept age / type=un subject=child g gcorr v vcorr;
run;

I cannot figure out how to reproduce this in R. I took a look at the
documentation as well as the first five chapters of Pinheiro & Bates
(2000)
without luck.

Cheers!

        [[alternative HTML version deleted]]

--
Kevin Wright