Sets longitudinal/clustered data correlation in Gaussian copula regression models.
cluster.cormat(id, type = c("independence", "ar1", "ma1",
"exchangeable", "unstructured"))An object of class cormat.gcmr representing a correlation matrix for longitudinal or clustered data.
subject id. This is a vector of the same lenght of the number of observations. Please note that data must be sorted in way that observations from the same cluster are contiguous.
a character string specifying the correlation structure. At the moment, the following are implemented:
independence | working independence. |
ar1 | autoregressive of order 1. |
ma1 | moving average of order 1. |
exchangeable | exchangeable. |
unstructured | unstructured. |
Guido Masarotto and Cristiano Varin.
The correlation matrices are inherited from the nlme package (Pinheiro and Bates, 2000).
Masarotto, G. and Varin, C. (2012). Gaussian copula marginal regression. Electronic Journal of Statistics 6, 1517--1549.
Masarotto, G. and Varin C. (2017). Gaussian Copula Regression in R. Journal of Statistical Software, 77(8), 1--26.
Pinheiro, J.C. and Bates, D.M. (2000). Mixed-Effects Models in S and S-PLUS. Springer.
gcmr, nlme.