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.
# 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,
...
)A fitted multi-state model produced by msm.
Minimum time at which to compute the observed and expected prevalences of states.
Maximum time at which to compute the observed and expected prevalences of states.
Initial time of the Markov process. Expected values are forecasted from here. Defaults to the minimum of the observation times given in the data.
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.
Interpolation method for observed states, see
prevalence.msm.
Subject-specific maximum follow-up times, see
prevalence.msm.
Vector of the subject identifiers to calculated observed prevalences for.
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".
(Misclassification models only) Values of covariates
on the misclassification probability matrix. See
prevalence.msm.
Times at which piecewise-constant intensities change.
See prevalence.msm.
Covariates on which the piecewise-constant
intensities depend. See prevalence.msm.
x axis label.
y axis label.
Line width for observed prevalences. See par.
Line width for expected prevalences. See par.
Line type for observed prevalences. See par.
Line type for expected prevalences. See par.
Line colour for observed prevalences. See par.
Line colour for expected prevalences. See par.
Vector of the \(x\) and \(y\) position, respectively, of the legend.
Further arguments to be passed to the generic plot
function.
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. Multi-state Markov models for analysing incomplete disease history data with illustrations for HIV disease. Statistics in Medicine (1994) 13(3): 805--821.
prevalence.msm