linVarReg: Linear mean and variance regression function

In VarReg: Semi-Parametric Variance Regression

Linear mean and variance regression function

Description

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(...),
  ...
)

Arguments

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

Value

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.

VarReg documentation built on May 31, 2023, 8:44 p.m.