hpc_estimation: Fit Joint Mean-Covariance Models based on HPC

In jmcm: Joint Mean-Covariance Models using 'Armadillo' and S4

Description

Fit joint mean-covariance models based on HPC.

Usage

hpc_estimation(m, Y, X, Z, W, start, mean, trace = FALSE, profile = TRUE,
  errormsg = FALSE, covonly = FALSE, optim_method = "default")

Arguments

m	an integer vector of numbers of measurements for subject.
Y	a vector of responses for all subjects.
X	model matrix for the mean structure model.
Z	model matrix for the diagonal matrix.
W	model matrix for the lower triangular matrix.
start	starting values for the parameters in the model.
mean	when covonly is true, it is used as the given mean.
trace	the values of the objective function and the parameters are printed for all the trace'th iterations.
profile	whether parameters should be estimated sequentially using the idea of profile likelihood or not.
errormsg	whether or not the error message should be print.
covonly	estimate the covariance structure only, and use given mean.
optim_method	optimization method, choose "default" or "BFGS"(vmmin in R).

See Also

mcd_estimation for joint mean covariance model fitting based on MCD, acd_estimation for joint mean covariance model fitting based on ACD.

jmcm documentation built on Jan. 16, 2021, 5:32 p.m.