Package index • msm

Summarising data

statetable.msm(): Table of transitions

Fitting models

msm(): Multi-state Markov and hidden Markov models in continuous time

crudeinits.msm(): Calculate crude initial values for transition intensities

hmmCat() hmmIdent() hmmUnif() hmmNorm() hmmLNorm() hmmExp() hmmGamma() hmmWeibull() hmmPois() hmmBinom() hmmBetaBinom() hmmNBinom() hmmBeta() hmmTNorm() hmmMETNorm() hmmMEUnif() hmmT(): Hidden Markov model constructors

hmmMV(): Multivariate hidden Markov models

msm2Surv(): Convert data for `msm' to data for `survival', `mstate' or `flexsurv' analysis

Output from fitted models

qmatrix.msm(): Transition intensity matrix

pmatrix.msm(): Transition probability matrix

pmatrix.piecewise.msm(): Transition probability matrix for processes with piecewise-constant intensities

sojourn.msm(): Mean sojourn times from a multi-state model

totlos.msm() envisits.msm(): Total length of stay, or expected number of visits

pnext.msm(): Probability of each state being next

ppass.msm(): Passage probabilities

efpt.msm(): Expected first passage time

qratio.msm(): Estimated ratio of transition intensities

hazard.msm(): Calculate tables of hazard ratios for covariates on transition intensities

coef(<msm>): Extract model coefficients

boot.msm(): Bootstrap resampling for multi-state models

ematrix.msm(): Misclassification probability matrix

odds.msm(): Calculate tables of odds ratios for covariates on misclassification probabilities

viterbi.msm(): Calculate the probabilities of underlying states and the most likely path through them

phasemeans.msm(): Parameters of phase-type models in mixture form

plot(<msm>): Plots of multi-state models

print(<msm>) printnew.msm(): Print a fitted msm model object

printold.msm(): Print a fitted msm model object

summary(<msm>): Summarise a fitted multi-state model

msm.form.qoutput() msm.form.eoutput(): Extract msm model parameter estimates in compact format

Tidy model outputs

tidy(<msm>): Tidy the parameter estimates from an msm model

tidy(<msm.est>): Tidy the output of pmatrix.msm and similar functions

tidy(<msm.estbystate>): Tidy the output of totlos.msm and similar functions

tidy(<msm.prevalence>): Tidy the output of prevalence.msm

Model checking and comparison

prevalence.msm(): Tables of observed and expected prevalences

plot(<prevalence.msm>): Plot of observed and expected prevalences

plot(<survfit.msm>): Plot empirical and fitted survival curves

plotprog.msm(): Kaplan Meier estimates of incidence

logLik(<msm>): Extract model log-likelihood

lrtest.msm(): Likelihood ratio test

pearson.msm(): Pearson-type goodness-of-fit test

draic.msm() drlcv.msm(): Criteria for comparing two multi-state models with nested state spaces

scoreresid.msm(): Score residuals

surface.msm() contour(<msm>) persp(<msm>) image(<msm>): Explore the likelihood surface

Simulation of data

sim.msm(): Simulate one individual trajectory from a continuous-time Markov model

simmulti.msm(): Simulate multiple trajectories from a multi-state Markov model with arbitrary observation times

simfitted.msm(): Simulate from a Markov model fitted using msm

Probability distributions

dpexp() ppexp() qpexp() rpexp(): Exponential distribution with piecewise-constant rate

dtnorm() ptnorm() qtnorm() rtnorm(): Truncated Normal distribution

d2phase() p2phase() q2phase() r2phase() h2phase(): Coxian phase-type distribution with two phases

dmenorm() pmenorm() qmenorm() rmenorm() dmeunif() pmeunif() qmeunif() rmeunif(): Measurement error distributions

qgeneric(): Generic function to find quantiles of a distribution

Datasets

cav: Heart transplant monitoring data

psor: Psoriatic arthritis data

aneur: Aortic aneurysm progression data

bos bos3 bos4: Bronchiolitis obliterans syndrome after lung transplants

fev: FEV1 measurements from lung transplant recipients

Miscellaneous utilities

deltamethod(): The delta method

MatrixExp(): Matrix exponential

model.frame(<msm>) model.matrix(<msm>): Extract original data from msm objects.

recreate.olddata(): Convert data stored in msm object to old format

transient.msm() absorbing.msm(): Transient and absorbing states

hmodel2list(): Convert a hmodel object to HMM constructor function calls

Package internals (advanced)

msm.object: Fitted msm model objects

cmodel.object: Developer documentation: censoring model object

qmodel.object: Developer documentation: transition model structure object

qcmodel.object: Developer documentation: model for covariates on transition intensities

emodel.object: Developer documentation: misclassification model structure object

ecmodel.object: Developer documentation: model for covariates on misclassification probabilities

hmodel.object: Developer documentation: hidden Markov model structure object

paramdata.object: Developer documentation: internal msm parameters object

updatepars.msm(): Update the maximum likelihood estimates in a fitted model object.

Package overview

msm-package: Multi-State Markov and Hidden Markov Models in Continuous Time