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msm (version 1.6.6)

Multi-State Markov and Hidden Markov Models in Continuous Time

Description

Functions for fitting continuous-time Markov and hidden Markov multi-state models to longitudinal data. Designed for processes observed at arbitrary times in continuous time (panel data) but some other observation schemes are supported. Both Markov transition rates and the hidden Markov output process can be modelled in terms of covariates, which may be constant or piecewise-constant in time.

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Install

install.packages('msm')

Monthly Downloads

15,206

Version

1.6.6

License

GPL (>= 2)

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Repository

https://github.com/chjackson/msm

Maintainer

Christopher Jackson

Last Published

February 2nd, 2018

Functions in msm (1.6.6)

Bootstrap resampling for multi-state models

Measurement error distributions

Developer documentation: model for covariates on transition intensities

Misclassification probability matrix

recreate.olddata

Convert data stored in msm object to old format

Parameters of phase-type models in mixture form

Probability of each state being next

Plots of multi-state models

Passage probabilities

pmatrix.piecewise.msm

Transition probability matrix for processes with piecewise-constant intensities

Score residuals

msm.form.qoutput

Extract msm model parameter estimates in compact format

model.frame.msm

Extract original data from msm objects.

Convert data for `msm' to data for `survival', `mstate' or `flexsurv' analysis

Tables of observed and expected prevalences

Psoriatic arthritis data

Multi-state Markov and hidden Markov models in continuous time

Calculate tables of odds ratios for covariates on misclassification probabilities

paramdata.object

Developer documentation: internal msm parameters object

plot.prevalence.msm

Plot of observed and expected prevalences

Pearson-type goodness-of-fit test

Developer documentation: misclassification model structure object

Simulate multiple trajectories from a multi-state Markov model with arbitrary observation times

FEV1 measurements from lung transplant recipients

plot.survfit.msm

Plot empirical and fitted survival curves

Generic function to find quantiles of a distribution

Exponential distribution with piecewise-constant rate

Print a fitted msm model object

Print a fitted msm model object

Table of transitions

Transition intensity matrix

Summarise a fitted multi-state model

Extract model log-likelihood

Simulate one individual trajectory from a continuous-time Markov model

Mean sojourn times from a multi-state model

Total length of stay, or expected number of visits

Simulate from a Markov model fitted using msm

Likelihood ratio test

Transient and absorbing states

Kaplan Meier estimates of incidence

Developer documentation: transition model structure object

Transition probability matrix

Estimated ratio of transition intensities

Truncated Normal distribution

Explore the likelihood surface

Calculate the probabilities of underlying states and the most likely path through them

The delta method

Coxian phase-type distribution with two phases

Calculate crude initial values for transition intensities

Bronchiolitis obliterans syndrome after lung transplants

Developer documentation: censoring model object

Heart transplant monitoring data

Matrix exponential

Aortic aneurysm progression data

Extract model coefficients

Expected first passage time

Calculate tables of hazard ratios for covariates on transition intensities

Criteria for comparing two multi-state models with nested state spaces

Developer documentation: hidden Markov model structure object

Multivariate hidden Markov models

Developer documentation: model for covariates on misclassification probabilities

Fitted msm model objects

Hidden Markov model constructors