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Plot of observed and expected prevalences

Source: R/prevalence.R
plot.prevalence.msm.Rd

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 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,
  ...
)

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.

See also

prevalence.msm