Provides a rough indication of goodness of fit of a multi-state model, by estimating the observed numbers of individuals occupying a state at a series of times, and plotting these against forecasts from the fitted model, for each state. Observed prevalences are indicated as solid lines, expected prevalences as dashed lines.

## Usage

```
# S3 method for prevalence.msm
plot(
x,
mintime = NULL,
maxtime = NULL,
timezero = NULL,
initstates = NULL,
interp = c("start", "midpoint"),
censtime = Inf,
subset = NULL,
covariates = "population",
misccovariates = "mean",
piecewise.times = NULL,
piecewise.covariates = NULL,
xlab = "Times",
ylab = "Prevalence (%)",
lwd.obs = 1,
lwd.exp = 1,
lty.obs = 1,
lty.exp = 2,
col.obs = "blue",
col.exp = "red",
legend.pos = NULL,
...
)
```

## Arguments

- x
A fitted multi-state model produced by

`msm`

.- mintime
Minimum time at which to compute the observed and expected prevalences of states.

- maxtime
Maximum time at which to compute the observed and expected prevalences of states.

- timezero
Initial time of the Markov process. Expected values are forecasted from here. Defaults to the minimum of the observation times given in the data.

- initstates
Optional vector of the same length as the number of states. Gives the numbers of individuals occupying each state at the initial time, to be used for forecasting expected prevalences. The default is those observed in the data. These should add up to the actual number of people in the study at the start.

- interp
Interpolation method for observed states, see

`prevalence.msm`

.- censtime
Subject-specific maximum follow-up times, see

`prevalence.msm`

.- subset
Vector of the subject identifiers to calculated observed prevalences for.

- covariates
Covariate values for which to forecast expected state occupancy. See

`prevalence.msm`

--- if this function runs too slowly, as it may if there are continuous covariates, replace`covariates="population"`

with`covariates="mean"`

.- misccovariates
(Misclassification models only) Values of covariates on the misclassification probability matrix. See

`prevalence.msm`

.- piecewise.times
Times at which piecewise-constant intensities change. See

`prevalence.msm`

.- piecewise.covariates
Covariates on which the piecewise-constant intensities depend. See

`prevalence.msm`

.- xlab
x axis label.

- ylab
y axis label.

- lwd.obs
Line width for observed prevalences. See

`par`

.- lwd.exp
Line width for expected prevalences. See

`par`

.- lty.obs
Line type for observed prevalences. See

`par`

.- lty.exp
Line type for expected prevalences. See

`par`

.- col.obs
Line colour for observed prevalences. See

`par`

.- col.exp
Line colour for expected prevalences. See

`par`

.- legend.pos
Vector of the \(x\) and \(y\) position, respectively, of the legend.

- ...
Further arguments to be passed to the generic

`plot`

function.

## Details

See `prevalence.msm`

for details of the assumptions underlying
this method.

Observed prevalences are plotted with a solid line, and expected prevalences with a dotted line.

## References

Gentleman, R.C., Lawless, J.F., Lindsey, J.C. and Yan, P.
Multi-state Markov models for analysing incomplete disease history data with
illustrations for HIV disease. *Statistics in Medicine* (1994) 13(3):
805--821.