stpm (version 1.1.2)

spm_discrete: Discrete multi-dimensional optimization

Description

Discrete multi-dimensional optimization

Usage

spm_discrete(dat, theta_range = seq(0.02, 0.2, by = 0.001), tol = NULL,
  verbose = FALSE)

Arguments

dat

A data table.

theta_range

A range of theta parameter (axe displacement of Gompertz function), default: from 0.001 to 0.09 with step of 0.001.

tol

A tolerance threshold for matrix inversion (NULL by default).

verbose

An indicator of verbosing output.

Value

• A list of two elements ("Ak205", "Ya2007"): (1) estimated parameters u, R, b, Sigma, Q, mu0, theta and (2) estimated parameters a, f1, Q, f, b, mu0, theta. Note: b and mu0 from first list are different from b and mu0 from the second list.

Details

This function is way much faster that continuous spm_continuous_MD(...) (but less precise) and used mainly in estimation a starting point for the spm_continuous_MD(...).

References

Akushevich I., Kulminski A. and Manton K. (2005), Life tables with covariates: Dynamic model for Nonlinear Analysis of Longitudinal Data. Mathematical Population Studies, 12(2), pp.: 51-80. .

Examples

library(stpm)
data <- simdata_discr(N=10)
#Parameters estimation
pars <- spm_discrete(data)
pars