stpm (version 1.1.2)

spm_continuous: Continuous multi-dimensional optimization

Description

Continuous multi-dimensional optimization

Usage

spm_continuous(dat, a = -0.05, f1 = 80, Q = 2e-08, f = 80, b = 5,
  mu0 = 2e-05, theta = 0.08, stopifbound = FALSE,
  algorithm = "NLOPT_LN_COBYLA", lb = NULL, ub = NULL, maxeval = 500,
  verbose = FALSE, pinv.tol = 0.01)

Arguments

dat

A data table.

a

A starting value of the rate of adaptive response to any deviation of Y from f1(t).

f1

A starting value of the average age trajectories of the variables which process is forced to follow.

Q

Starting values of the quadratic hazard term.

f

A starting value of the "optimal" value of variable which corresponds to the minimum of hazard rate at a respective time.

b

A starting value of a diffusion coefficient representing a strength of the random disturbance from Wiener Process.

mu0

A starting value of the baseline hazard.

theta

A starting value of the parameter theta (axe displacement of Gompertz function).

stopifbound

Estimation stops if at least one parameter achieves lower or upper boundaries.

algorithm

An optimization algorithm used, can be one of those provided by nloptr. #'Check the NLopt website for a description of the algorithms. Default: NLOPT_LN_COBYLA

lb

Lower bound of parameters under estimation.

ub

Upper bound of parameters under estimation.

maxeval

Maximum number of iterations of the algorithm for nloptr optimization. The program stops when the number of function evaluations exceeds maxeval. Default: 500.

verbose

An indicator of verbosing output.

pinv.tol

A tolerance value for pseudo-inverse of matrix gamma (see Yashin, A.I. et al (2007). Stochastic model for analysis of longitudinal data on aging and mortality. Mathematical Biosciences, 208(2), 538-551..)

Value

• A set of estimated parameters a, f1, Q, f, b, mu0, theta and additional variable limit which indicates if any parameter achieved lower or upper boundary conditions (FALSE by default).

Details

spm_continuous runs much slower that discrete but more precise and can handle time intervals with different lengths.

References

Yashin, A.I. et al (2007). Stochastic model for analysis of longitudinal data on aging and mortality. Mathematical Biosciences, 208(2), 538-551..

Examples

library(stpm)
#Reading the data:
data <- simdata_cont(N=2)
head(data)
#Parameters estimation:
pars <- spm_continuous(dat=data,a=-0.05, f1=80, 
					 Q=2e-8, f=80, b=5, mu0=2e-5, theta=0.08)
pars