corMatrix.corStruct: Matrix of a corStruct Object

Description Usage Arguments Value Author(s) References See Also Examples

Description

This method function extracts the correlation matrix (or its transpose inverse square-root factor), or list of correlation matrices (or their transpose inverse square-root factors) corresponding to `covariate` and `object`. Letting S denote a correlation matrix, a square-root factor of S is any square matrix L such that S=L'L. When `corr = FALSE`, this method extracts L^(-t).

Usage

 ```1 2``` ```## S3 method for class 'corStruct' corMatrix(object, covariate, corr, ...) ```

Arguments

 `object` an object inheriting from class `"corStruct"` representing a correlation structure. `covariate` an optional covariate vector (matrix), or list of covariate vectors (matrices), at which values the correlation matrix, or list of correlation matrices, are to be evaluated. Defaults to `getCovariate(object)`. `corr` a logical value. If `TRUE` the function returns the correlation matrix, or list of correlation matrices, represented by `object`. If `FALSE` the function returns a transpose inverse square-root of the correlation matrix, or a list of transpose inverse square-root factors of the correlation matrices. `...` some methods for this generic require additional arguments. None are used in this method.

Value

If `covariate` is a vector (matrix), the returned value will be an array with the corresponding correlation matrix (or its transpose inverse square-root factor). If the `covariate` is a list of vectors (matrices), the returned value will be a list with the correlation matrices (or their transpose inverse square-root factors) corresponding to each component of `covariate`.

Author(s)

JosÃ© Pinheiro and Douglas Bates bates@stat.wisc.edu

References

Pinheiro, J.C., and Bates, D.M. (2000) "Mixed-Effects Models in S and S-PLUS", Springer.

`corFactor.corStruct`, `Initialize.corStruct`

Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30``` ```cs1 <- corAR1(0.3) corMatrix(cs1, covariate = 1:4) corMatrix(cs1, covariate = 1:4, corr = FALSE) # Pinheiro and Bates, p. 225 cs1CompSymm <- corCompSymm(value = 0.3, form = ~ 1 | Subject) cs1CompSymm <- Initialize(cs1CompSymm, data = Orthodont) corMatrix(cs1CompSymm) # Pinheiro and Bates, p. 226 cs1Symm <- corSymm(value = c(0.2, 0.1, -0.1, 0, 0.2, 0), form = ~ 1 | Subject) cs1Symm <- Initialize(cs1Symm, data = Orthodont) corMatrix(cs1Symm) # Pinheiro and Bates, p. 236 cs1AR1 <- corAR1(0.8, form = ~ 1 | Subject) cs1AR1 <- Initialize(cs1AR1, data = Orthodont) corMatrix(cs1AR1) # Pinheiro and Bates, p. 237 cs1ARMA <- corARMA(0.4, form = ~ 1 | Subject, q = 1) cs1ARMA <- Initialize(cs1ARMA, data = Orthodont) corMatrix(cs1ARMA) # Pinheiro and Bates, p. 238 spatDat <- data.frame(x = (0:4)/4, y = (0:4)/4) cs1Exp <- corExp(1, form = ~ x + y) cs1Exp <- Initialize(cs1Exp, spatDat) corMatrix(cs1Exp) ```

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