Stochastic Process Model for Analysis of Longitudinal and Time-to-Event Outcomes

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Documentation for package ‘stpm’ version 1.7.12

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stpm-package Stochastic Process Model for Analysis of Longitudinal and Time-to-Event Outcomes
assign_to_global function loading results in global environment
ex_data This is the longitudinal genetic dataset.
func1 An internal function to compute m and gamma based on continuous-time model (Yashin et. al., 2007)
get.column.index An internal function to obtain column index by its name
getNextY.cont An internal function to compute next Y based on continous-time model (Yashin et. al., 2007)
getNextY.cont2 An internal function to compute next value of physiological variable Y
getNextY.discr An internal function to compute the next value of physiological variable Y based on discrete-time model (Akushevich et. al., 2005)
getNextY.discr.m An internal function to compute next m based on dicrete-time model
getPrevY.discr An internal function to compute previous value of physiological variable Y based on discrete-time model
getPrevY.discr.m An internal function to compute previous m based on discrete-time model
longdat This is the longitudinal dataset.
LRTest Likelihood-ratio test
m An internal function to compute m from
make.short.format An internal function which construct short data format from a given long
mu An internal function to compute mu
prepare_data Data pre-processing for analysis with stochastic process model methodology.
prepare_data_cont Prepares continuouts-time dataset.
prepare_data_discr Prepares discrete-time dataset.
sigma_sq An internal function to compute sigma square analytically
simdata_cont Multi-dimensional simulation function for continuous-time SPM.
simdata_discr Multi-dimension simulation function
simdata_gamma_frailty This script simulates data using familial frailty model. We use the following variation: gamma(mu, ssq), where mu is the mean and ssq is sigma square. See: https://www.rocscience.com/help/swedge/webhelp/swedge/Gamma_Distribution.htm
simdata_time_dep Simulation function for continuous trait with time-dependant coefficients.
sim_pobs Multi-dimension simulation function for data with partially observed covariates (multidimensional GenSPM) with arbitrary intervals
spm A central function that estimates Stochastic Process Model parameters a from given dataset.
spm.impute Multiple Data Imputation with SPM
spm_continuous Continuous multi-dimensional optimization
spm_cont_lin Continuous multi-dimensional optimization with linear terms in mu only
spm_cont_quad_lin Continuous multi-dimensional optimization with quadratic and linear terms
spm_con_1d Fitting a 1-D SPM model with constant parameters
spm_con_1d_g Fitting a 1-D genetic SPM model with constant parameters
spm_discrete Discrete multi-dimensional optimization
spm_pobs Continuous-time multi-dimensional optimization for SPM with partially observed covariates (multidimensional GenSPM)
spm_projection A data projection with previously estimated or user-defined parameters. Projections are constructed for a cohort with fixed or normally distributed initial covariates.
spm_time_dep A function for the model with time-dependent model parameters.
stpm Stochastic Process Model for Analysis of Longitudinal and Time-to-Event Outcomes
trim Returns string w/o leading or trailing whitespace
trim.leading Returns string w/o leading whitespace
trim.trailing Returns string w/o trailing whitespace
vitstat Vital (mortality) statistics.