objective.function.Exponential: Objective function for the Exponential deconvolution

In mbannick/survDeconvolution: Survival Deconvolution

Description

Returns the objective function evaluated with the current parameters and observed data. Used to perform non-linear least squares. This objective function is specific to Exponential deconvolution, so it requires 3 parameters (one for each Weibull and one for the frailty).

Usage

objective.function.Exponential(params, y, t_y, z, t_z, control_list,
  alpha = 1, epsilon = epsilon, correlated = TRUE, int.upper = Inf)

Arguments

params	A list of parameters in the order lambda, mu, beta
y	A vector of observations representing survival at t_y
t_y	A vector of times at which survival is observed for y
z	A vector of observations representing survival at t_z
t_z	A vector of times at which survival is observed for z
control_list	A named list of options to be passed to distrExIntegrate, called subdivisions and rel.tol.
alpha	A number, usually fixed to 1, that is the shape parameter of the frailty distribution
epsilon	The smallest value from which to integrate, passed to integration functions, for approximations
correlated	If TRUE, then y and x = z - y are correlated through a shared frailty term with rate parameter beta. If FALSE, then they are independent and the function ignores beta.
int.upper	The largest value from which to integrate (in place of Inf) for approximations

Value

A float, the objective function evaluated at the current parameters

mbannick/survDeconvolution documentation built on Sept. 30, 2020, 9:22 a.m.