spm | R Documentation |
A central function that estimates Stochastic Process Model parameters a from given dataset.
spm( x, model = "discrete", formulas = list(at = "a", f1t = "f1", Qt = "Q", ft = "f", bt = "b", mu0t = "mu0"), start = NULL, tol = NULL, stopifbound = FALSE, lb = NULL, ub = NULL, pinv.tol = 0.01, theta.range = seq(0.01, 0.2, by = 0.001), verbose = FALSE, gomp = FALSE, opts = list(algorithm = "NLOPT_LN_NELDERMEAD", maxeval = 100, ftol_rel = 1e-08) )
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.
Default: |
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. |
lb |
Lower boundary, default |
ub |
Upper boundary, default |
pinv.tol |
A tolerance threshold for matrix pseudo-inverse. Default: 0.01. |
theta.range |
A user-defined range of the parameter |
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 = mu0*exp(theta*t), Q = Q*exp(theta*t). |
opts |
A list of options for |
For "discrete" (dmodel) and "continuous" (cmodel) 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.
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>.
## 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)
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