Linear mean and variance regression function

linVarReg performs multivariate mean and multivariate variance regression. This function is designed to be used by the semiVarReg function.

Usage

linVarReg(
  dat,
  var.ind = c(2),
  mean.ind = c(2),
  para.space = c("all", "positive", "negative"),
  control = list(...),
  ...
)

dat: Dataframe containing outcome and covariate data. Outcome data must be in the first column. Covariates for mean and variance model in next columns.
var.ind: Vector containing the column numbers of the data in 'dat' to be fit as covariates in the variance model. FALSE indicates constant variance option.
mean.ind: Vector containing the column numbers of the data in 'dat' to be fit as covariates in the mean model. 0 indicates constant mean option. NULL indicates zero mean option.
para.space: Parameter space to search for variance parameter estimates. "positive" means only search positive parameter space, "negative" means search only negative parameter space and "all" means search all.
control: List of control parameters. See VarReg.control.
...: arguments to be used to form the default control argument if it is not supplied directly

linVarReg returns a list of output including:

converged: Logical argument indicating if convergence occurred.
iterations: Total iterations performed of the EM algorithm.
reldiff: the positive convergence tolerance that occured at the final iteration.
loglik: Numeric variable of the maximised log-likelihood.
boundary: Logical argument indicating if estimates are on the boundary.
aic.c: Akaike information criterion corrected for small samples
aic: Akaike information criterion
bic: Bayesian information criterion
hqc: Hannan-Quinn information criterion
mean.ind: Vector of integer(s) indicating the column number(s) in the dataframe data that were fit in the mean model.
mean: Vector of the maximum likelihood estimates of the mean parameters.
var.ind: Vector of integer(s) indicating the column(s) in the dataframe data that were fit in the variance model.
variance: Vector of the maximum likelihood estimates of the variance parameters.
cens.ind: Integer indicating the column in the dataframe data that corresponds to the censoring indicator. Always NULL.
data: Dataframe containing the variables included in the model.