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
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