cluster.cormat {gcmr}

Longitudinal/Clustered Data Correlation

Description

Sets longitudinal/clustered data correlation in Gaussian copula regression models.

Usage

cluster.cormat(id, type = c("independence", "ar1", "ma1", 
		   	    "exchangeable", "unstructured"))

Arguments

id

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.

type

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.

Details

The correlation matrices are inherited from the nlme package (Pinheiro and Bates, 2000).

Value

An object of class cormat.gcmr representing a correlation matrix for longitudinal or clustered data.

Author(s)

Guido Masarotto and Cristiano Varin.

References

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.

See Also

gcmr, nlme.