# covjmcm: Estimate the covariance of estimated parameters using the... In varjmcm: Estimations for the Covariance of Estimated Parameters in Joint Mean-Covariance Models

## 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

 `1` ```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.

`covjmcm_mcd`, `covjmcm_acd`, and `covjmcm_hpc`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13``` ```## 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) ```