spm_discrete | R Documentation |
Discrete multi-dimensional optimization
spm_discrete( dat, theta_range = seq(0.02, 0.2, by = 0.001), tol = NULL, verbose = FALSE )
dat |
A data table. |
theta_range |
A range of |
tol |
A tolerance threshold for matrix inversion (NULL by default). |
verbose |
An indicator of verbosing output. |
This function is way more faster that continuous spm_continuous_MD(...)
(but less precise) and used mainly in
estimation a starting point for the spm_continuous_MD(...)
.
A list of two elements ("dmodel", "cmodel"): (1) estimated parameters u, R, b, Sigma, Q, mu0, theta for discrete-time model and (2) estimated parameters a, f1, Q, f, b, mu0, theta for continuous-time model. Note: b and mu0 from first list are different from b and mu0 from the second list.
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. <DOI:10.1080/08898480590932296>.
library(stpm) data <- simdata_discr(N=10) #Parameters estimation pars <- spm_discrete(data) pars
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