dD.hat: Obtain the matrix of partial derivatives of the sample...

In depCensoring: Statistical Methods for Survival Data with Dependent Censoring

Obtain the matrix of partial derivatives of the sample variances.

Description

This function computes the matrix of sample derivatives of the sample variances.

Usage

dD.hat(
  data,
  beta,
  t,
  hp,
  mi.mat = NULL,
  m.avg = NULL,
  dm.avg = NULL,
  dmi.tens = NULL
)

Arguments

data	Data frame.
beta	Vector of coefficients.
t	Time point at which to evaluate the (derivatives of) the moment functions. Also allowed to be a vector of time points (used in estimating the model under assumed time- independent coefficients).
hp	List of hyperparamerers.
mi.mat	A precomputed matrix of moment function evaluations at each observation. If supplied, some computations can be skipped. Default is mi.mat = NULL.
m.avg	A precomputed vector of the sample average of the moment functions. If not supplied, this vector is computed. Default is m.avg = NULL.
dm.avg	Matrix of precomputed sample averages of the derivatives of the moment functions. Default is dm.avg = NULL.
dmi.tens	3D tensor of precomputed evaluations of the derivatives of the moment functions. Default is dmi.tens = NULL.

Value

A matrix containing the partial derivatives of the variances of the moment functions. Each row corresponds to a moment function, each column corresponds to a covariate.

depCensoring documentation built on April 4, 2025, 1:52 a.m.