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Below there are two examples of multiple data imputation with function spm. The package offers following five hypotheses to test for function Arbeev et al. Akushevich, I.

SIAM Journal on Control and Optimization

Kulminski, and K. Informa UK Limited: 51— Arbeev, Konstantin G. Kulminski, Svetlana V. Ukraintseva, and Anatoliy I. Frontiers Media SA. Kulminski, Liubov S.

Deterministic vs stochastic trends

Arbeeva, Lucy Akushevich, Svetlana V. Ukraintseva, Irina V.

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Culminskaya, and Anatoli I. Elsevier BV: — Cohen, Liubov S. Kulminski, Igor Akushevich, Svetlana V.

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Ukraintseva, Kaare Christensen, and Anatoliy I. Arbeev, Anatoliy I. Yashin, and Alexander M.

Modeling and Simulation

Wiley: — Woodbury, Max A. Elsevier BV: 37— Yashin, Anatoli I.


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Springer Nature: — Zhbannikov Data description Data represents a typical longitudinal data in form of two datasets: longitudinal dataset follow-up studies , in which one record represents a single observation, and vital survival statistics, where one record represents all information about the subject. Age - current age of subject at observation. Discrete- and continuous-time models There are two main SPM types in the package: discrete-time model Akushevich, Kulminski, and Manton and continuous-time model A.

Two covariates This is an example for two physiological variables covariates. Simulation individual trajectory projection, also known as microsimulations We added one- and multi- dimensional simulation to be able to generate test data for hyphotesis testing. This is a vector with length of k Q - A matrix of k by k , which is a non-negative-definite symmetric matrix f - A vector-function with length k of the normal or optimal state b - A diffusion coefficient, k by k matrix mu0 - mortality at start period of time baseline hazard theta - A displacement coefficient of the Gompertz function ystart - A vector with length equal to number of dimensions used, defines starting values of covariates tstart - A number that defines a start time 30 by default.

SPM with partially observed covariates Stochastic Process Model has many applications in analysis of longitudinal biodemographic data. Joint analysis of two datasets: first dataset with genetic and second dataset with non-genetic component library stpm data. Multiple imputation with spm. Prediction We provide a simple function to predict the next value of.