[R] Measuring correlations in repeated measures data

[Brant Inman]

R-helpers:

I would like to measure the correlation coefficient between the repeated measures of a single variable that is measured over time and is unbalanced.  As an example, consider the Orthodont dataset from package nlme, where the model is:

fit <- lmer(distance ~ age + (1 | Subject), data=Orthodont)

I would like to measure the correlation b/t the variable "distance" at different ages such that I would have a matrix of correlation coefficients like the following:

       age08 age09 age10 age11 age12 age13 age14
age08    1
age09          1
age10                1
age11                      1
age12                            1
age13                                  1
age14                                         1

The idea would be to demonstrate that the correlations b/t repeated measures of the variable "distance" decrease as the time b/t measures increases.  For example, one might expect the correlation coefficient b/t age08 and age09 to be higher than that between age08 and age14. 

Is there a function that can calculate such correlation coefficients of a repeatedly measured variable "y" ("distance" in the Orthodont dataset) across some category "x" ("age" in the Orthodont dataset)?

Thanks,

Brant