Package index • msmbayes

Exploratory data analysis

statetable(): Summarise intermittenly-observed multi-state data
msmhist(): Illustrate the empirical distribution of states against time in intermittently-observed multistate data
msmhist_bardata(): Estimate state occupation probabilities to be illustrated by a bar plot in msmhist

qdf(): Transition intensities from an msmbayes model, presented as a tidy data frame
qmatrix(): Transition intensity matrix from an msmbayes model
pmatrixdf(): Transition probabilities from an msmbayes model, presented as a tidy data frame
pmatrix(): Transition probability matrix from an msmbayes model
pnext(): Probabilities for the next state in a multi-state model
mean_sojourn(): Mean sojourn times from an msmbayes model
loghr() hr(): (Log) hazard ratios for covariates on transition intensities
soj_prob(): Sojourn probability in a state of a msmbayes model
soj_haz(): Hazard function of the sojourn distribution in a state of a msmbayes model
soj_quantile(): Quantiles of the sojourn distribution in a state of a msmbayes model
totlos(): Total length of stay in each state over an interval
loglik() logLik(<msmbayes>): Log likelihood from an msmbayes model
summary(<msmbayes>): Summarise basic parameter estimates from an msmbayes model
standardise_to() standardize_to(): Constructor for a standardising population used for model outputs

phaseapprox_pars(): Summarise posteriors for shape and scale parameters for the sojourn distribution in a semi-Markov msmbayes model
logtaf() taf(): (Log) time acceleration factors in semi-Markov models
logoddsnext(): Summarise posteriors for log odds of transitions from phase-type states
logrrnext() rrnext(): Effects of covariates on competing exit transitions in phase type models

edf(): Misclassification error probabilities from an msmbayes model
ematrix(): Matrix of misclassification error probabilities from an msmbayes model

msmbayes_prior_sample(): Generate a sample from the prior distribution in a msmbayes model
msmbayes_priorpred_sample(): Generate a dataset from the prior predictive distribution in a msmbayes model
sim_2state_smm(): Simulate intermittently-observed data from a semi-Markov multi-state model with two states and reversible transitions.

dnphase() pnphase() hnphase() mean_nphase() var_nphase() skewness_nphase() ncmoment_nphase() rnphase() qnphase(): Density, probability distribution, quantile, moment, hazard and random number generation functions for the Coxian phase-type distribution with any number of phases.
canpars_to_rates() rates_to_canpars(): Convert between canonical parameters and rates for a phase-type distribution
shapescale_to_rates(): Determine parameters of a phase-type model that approximate a parametric shape-scale distribution
qphaseapprox(): Phase-type expansion of a transition intensity matrix to create a non-Markov multi-state model
nphase_Q(): Given a phase-type sojourn distribution, return the corresponding Markov intensity matrix where the last state is the absorbing state, and the the time to absorption is the sojourn distribution.
shape_ubound(): Upper bound for shape parameter in moment-based phase-type approximations
gamma_shape_in_bounds() weibull_shape_in_bounds(): Test whether a shape parameter of is in the bounds required for a valid phase-type approximation
n3_moment_bounds(): Bounds on normalised moments for phase-type approximations

infsim infsim2: Simulated infection testing data
bigdat: A simulated multistate dataset with lots of observations and covariates
infsim_model infsim_modelc infsim_modelp infsim_modelpc: Example fitted model objects used for testing msmbayes
illdeath_data: A simulated dataset from an illness-death model

msmbayes-package: The 'msmbayes' package for Bayesian multi-state modelling of intermittently-observed data