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R: A central function that estimates Stochastic Process Model...

spm	R Documentation

A central function that estimates Stochastic Process Model parameters a from given dataset.

Description

A central function that estimates Stochastic Process Model parameters a from given dataset.

Usage

spm(x, model = "discrete", formulas = NULL, start = NULL, tol = NULL,
  stopifbound = FALSE, algorithm = "NLOPT_LN_NELDERMEAD", lb = NULL,
  ub = NULL, maxeval = 100, pinv.tol = 0.01, theta.range = seq(0.01,
  0.2, by = 0.001), verbose = FALSE, gomp = FALSE)

Arguments

`x`	A dataset: is the output from prepare_data(...) function and consists of two separate data tables: (1) a data table for continuous-time model and (2) a data table for discrete-time model.
`model`	A model type. Choices are: "discrete", "continuous" or "time-dependent".
`formulas`	A list of parameter formulas used in the "time-dependent" model.
`start`	A starting values of coefficients in the "time-dependent" model.
`tol`	A tolerance threshold for matrix inversion (NULL by default).
`stopifbound`	A flag (default=FALSE) if it is set then the optimization stops when any of the parametrs achives lower or upper boundary.
`algorithm`	An optimization algorithm used in `nloptr` package. Default: `NLOPTR_NL_NELDERMEAD`.
`lb`	Lower boundary, default `NULL`.
`ub`	Upper boundary, default `NULL`.
`maxeval`	Maximum number of evaluations of optimization algorithm. Default 100.
`pinv.tol`	A tolerance threshold for matrix pseudo-inverse. Default: 0.01.
`theta.range`	A user-defined range of the parameter `theta` used in discrete-time optimization and estimating of starting point for continuous-time optimization.
`verbose`	A verbosing output indicator (FALSE by default).
`gomp`	A flag (FALSE by default). When it is set, then time-dependent exponential form of mu0 and Q are used: mu0 = mu0exp(thetat), Q = Qexp(thetat).

Value

For "discrete" and "continuous" model types: (1) a list of model parameter estimates for the discrete model type described in "Life tables with covariates: Dynamic Model for Nonlinear Analysis of Longitudinal Data", Akushevich et al, 2005.<DOI:10.1080/08898480590932296>, and (2) a list of model parameter estimates for the continuous model type described in "Stochastic model for analysis of longitudinal data on aging and mortality", Yashin et al, 2007, Math Biosci.<DOI:10.1016/j.mbs.2006.11.006>.

For the "time-dependent" model (model parameters depend on time): a set of model parameter estimates.

References

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

Akushevich I., Kulminski A. and Manton K. (2005). Life tables with covariates: Dynamic model for Nonlinear Analysis of Longitudinal Data. Mathematical Popu-lation Studies, 12(2), pp.: 51-80. <DOI: 10.1080/08898480590932296>.

Yashin, A. et al (2007), Health decline, aging and mortality: how are they related? Biogerontology, 8(3), 291-302.<DOI:10.1007/s10522-006-9073-3>.

Examples

## Not run:  
library(stpm)
data.continuous <- simdata_cont(N=1000)
data.discrete <- simdata_discr(N=1000)
data <- list(data.continuous, data.discrete)
p.discr.model <- spm(data)
p.discr.model
p.cont.model <- spm(data, model="continuous")
p.cont.model
p.td.model <- spm(data, 
model="time-dependent",f=list(at="aa*t+bb", f1t="f1", Qt="Q", ft="f", bt="b", mu0t="mu0"), 
start=list(a=-0.001, bb=0.05, f1=80, Q=2e-8, f=80, b=5, mu0=1e-3))
p.td.model

## End(Not run)

A central function that estimates Stochastic Process Model parameters a from given dataset.

Description

Usage

Arguments

Value

References

Examples

Results