R: Simulate from a Markov model fitted using msm

simfitted.msm {msm} | R Documentation

## Simulate from a Markov model fitted using msm

### Description

Simulate a dataset from a Markov model fitted using `msm`, using the maximum likelihood estimates as parameters, and the same observation times as in the original data.

### Usage

    simfitted.msm(x, drop.absorb=TRUE, drop.pci.imp=TRUE)

### Arguments

`x` | A fitted multi-state model object as returned by `msm`.
`drop.absorb` | Should repeated observations in an absorbing state be omitted. Use the default of `TRUE` to avoid warnings when using the simulated dataset for further `msm` fits. Or set to `FALSE` if exactly the same number of observations as the original data are needed.
`drop.pci.imp` | In time-inhomogeneous models fitted using the `pci` option to `msm`, censored observations are inserted into the data by `msm` at the times where the intensity changes, but dropped by default when simulating from the fitted model using this function. Set this argument to `FALSE` to keep these observations and the corresponding indicator variable.

### Details

This function is a wrapper around `simmulti.msm`, and only simulates panel-observed data. To generate datasets with the exact times of transition, use the lower-level `sim.msm`.

Markov models with misclassified states fitted through the `ematrix` option to `msm` are supported, but not general hidden Markov models with `hmodel`. For misclassification models, this function includes misclassification in the simulated states.

This function is used for parametric bootstrapping to estimate the null distribution of the test statistic in `pearson.msm`.

### Value

A dataset with variables as described in `simmulti.msm`.

### Author(s)

C. H. Jackson chris.jackson@mrc-bsu.cam.ac.uk

### See Also

`simmulti.msm`, `sim.msm`, `pearson.msm`, `msm`.

[Package msm version 1.6.7 Index]