estReg_single: Bayesian stimation in nonlinear regression models

In SimoneHermann/hierRegSDE:

Description

Bayesian estimation of the parameters of the mixed nonlinear regression model y_j= f(φ, t_j) + ε_j, ε_j~N(0,γ^2*s^2(t_j).

Usage

estReg_single(t, y, prior, start, fODE, sVar, len = 1000, mod = "Gompertz")

Arguments

t	vector of observation times
y	vector of the M trajectories
prior	list of prior parameters - list(mu, Omega, alpha, beta)
start	list of starting values
fODE	regression function
sVar	variance function
len	number of iterations of the MCMC algorithm
mod	model out of Gompertz, Richards, logistic, Weibull, only used instead of fODE
propSd	proposal standard deviation of phi is |mu|*propSd

Value

phi	estimator of φ
gamma2	estimator of γ^2

SimoneHermann/hierRegSDE documentation built on May 9, 2019, 1:46 p.m.