censloop_em: The Censored data EM loop
In VarReg: Semi-Parametric Variance Regression

censloop_em

R Documentation

The Censored data EM loop

censloop_em is an EM loop function for censored data to be utilised by various other higher level functions.

censloop_em(
  meanmodel,
  theta.old,
  beta.old,
  p.old,
  x.0,
  X,
  censor.ind,
  mean.intercept,
  maxit,
  eps
)

`meanmodel`	Dataframe containing only the covariates to be fit in the mean model. NULL for zero mean model and FALSE for constant mean model.
`theta.old`	Vector containing the initial variance parameter estimates to be fit in the variance model.
`beta.old`	Vector containing the initial mean parameter estimates to be fit in the mean model.
`p.old`	Vector of length n containing the initial variance estimate.
`x.0`	Matrix of covariates (length n) to be fit in the variance model. All have been rescaled so zero is the minimum. If NULL, then its a constant variance model.
`X`	Vector of length n of the outcome variable.
`censor.ind`	Vector of length n of the censoring indicator. 0=uncensored, -1=left censored and 1 is right censored.
`mean.intercept`	Logical to indicate if mean intercept is to be included in the model.
`maxit`	Number of maximum iterations for the EM algorithm.
`eps`	Very small number for the convergence criteria.

A list of the results from the EM algorithm, including:

conv: Logical argument indicating if convergence occurred
it: Total iterations performed of the EM algorithm
reldiff: the positive convergence tolerance that occured at the final iteration.
theta.new: Vector of variance parameter estimates. Note that these are not yet transformed back to the appropriate scale
mean: Vector of mean parameter estimates
fittedmean: Vector of fitted mean estimates
p.old: Vector of fitted variance estimates