Description Usage Arguments Details References See Also
Provides a rough indication of goodness of fit of a multistate 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.
1 2 3 4 5 6 7 8 9 10  ## S3 method for class '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,
...)

x 
A fitted multistate model produced by 
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

censtime 
Subjectspecific maximum followup times, see 
subset 
Vector of the subject identifiers to calculated observed prevalences for. 
covariates 
Covariate values for which to forecast expected
state occupancy. See 
misccovariates 
(Misclassification models only) Values of covariates on the misclassification
probability matrix. See 
piecewise.times 
Times at which piecewiseconstant intensities
change. See 
piecewise.covariates 
Covariates on which the piecewiseconstant
intensities depend. See 
xlab 
x axis label. 
ylab 
y axis label. 
lwd.obs 
Line width for observed prevalences. See 
lwd.exp 
Line width for expected prevalences. See 
lty.obs 
Line type for observed prevalences. See 
lty.exp 
Line type for expected prevalences. See 
col.obs 
Line colour for observed prevalences. See 
col.exp 
Line colour for expected prevalences. See 
legend.pos 
Vector of the x and y position, respectively, of the legend. 
... 
Further arguments to be passed to the generic 
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.
Gentleman, R.C., Lawless, J.F., Lindsey, J.C. and Yan, P. Multistate Markov models for analysing incomplete disease history data with illustrations for HIV disease. Statistics in Medicine (1994) 13(3): 805–821.
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