stpm | R Documentation |
Utilities to estimate parameters of the models with survival functions induced by stochastic covariates. Miscellaneous functions for data preparation and simulation are also provided. For more information, see: "Stochastic model for analysis of longitudinal data on aging and mortality" by Yashin A. et al, 2007, Mathematical Biosciences, 208(2), 538-551 <DOI:10.1016/j.mbs.2006.11.006>.
I. Y. Zhbannikov, Liang He, K. G. Arbeev, I. Akushevich, A. I. Yashin.
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) #Prepare data for optimization data <- prepare_data(x=system.file("extdata","longdat.csv",package="stpm"), covariates="BMI") #Parameters estimation (default model: discrete-time): p.discr.model <- spm(data) p.discr.model # Continuous-time model: p.cont.model <- spm(data, model="continuous") p.cont.model #Model with time-dependent coefficients: data <- prepare_data(x=system.file("extdata","longdat.csv",package="stpm"), covariates="BMI") p.td.model <- spm(data, model="time-dependent") p.td.model ## End(Not run)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.