covjmcm {varjmcm}

Estimate the covariance of estimated parameters using the explicit formula

Description

covjmcm is a combination of covjmcm_mcd, covjmcm_acd, and covjmcm_hpc. It identifies the corresponding type of the model, i.e. MCD, ACD, or HPC, and calculates the estimation of the covariance of estimated parameters using explicit formula, which is the inverse of the estimated Fisher's information matrix.

Usage

covjmcm(object)

Arguments

object

a fitted joint mean-covariance model of class "jmcmMod", returned by the function jmcm.

Value

an estimated covariance matrix of the estimated parameters.

References

[1] Pourahmadi, M., "Maximum likelihood estimation of generalised linear models for multivariate normal covariance matrix," Biometrika 87(2), 425–435 (2000).

[2] M. Maadooliat, M. Pourahmadi and J. Z. Huang, "Robust estimation of the correlation matrix of longitudinal data", Statistics and Computing 23, 17-28, (2013).

[3] W. Zhang, C. Leng, and C. Y. Tang(2015), "A joint modelling approach for longitudinal studies," Journal of the Royal Statistical Society. Series B. 77, 219-238.

See Also

covjmcm_mcd, covjmcm_acd, and covjmcm_hpc

Examples

## balanced data
cattleA <- cattle[cattle$group=='A', ]
fit.mcd <- jmcm(weight|id|I(ceiling(day/14+1))~1|1,
               data = cattleA, cov.method = "mcd",
               triple = c(8,3,4))
cov.mcd <- covjmcm(fit.mcd)  ##same as covjmcm_mcd(fit.mcd)
## unbalanced data
## This may take about 1.25 min.

fit.hpc <- jmcm(I(sqrt(cd4)) | id | time ~ 1 | 1,
                data = aids, triple = c(8,1,1),
                cov.method = "hpc")
cov.hpc <- covjmcm(fit.hpc)  ##same as covjmcm_hpc(fit.hpc)