prevplot: Plot observed and expected prevalences for a multi-state...
In msmtools: Building Augmented Data to Run Multi-State Models with 'msm' Package

Description Usage Arguments Details Author(s) References See Also Examples

View source: R/prevplot.R

Provides a graphical indication of goodness of fit of a multi-state model computed by msm using observed and expected prevalences. It also computes a rough indicator of where the data depart from the estimated Markov model.

1	prevplot(x, prev.obj, exacttimes = TRUE, M = FALSE, ci = FALSE)

`x`	A `msm` object.
`prev.obj`	A list computed by `prevalence.msm`. It can be with or without confidence intervals. `prevplot` will behaves accordingly.
`exacttimes`	If `TRUE` (default) then transition times are known and exact. This is inherited from `msm` and should be set the same way.
`M`	If `TRUE`, then a rough indicator of deviance from the model is computed (see 'Details'). Default is `FALSE`.
`ci`	If `TRUE`, then confidence intervals, if they exist, are plotted. Default is `FALSE`.

When M = TRUE, a rough indicator of the deviance from the Markov model is computed according to Titman and Sharples (2008). A comparison at a given time t_i of a patient k in the state s between observed counts O_{is} with expected ones E_{is} is build as follows:

(O_{is} - E_{is})^2 / E_{is}

The plot of the deviance M is returned together with the standard prevalence plot in the second row. This is not editable by the user.

Francesco Grossetti francesco.grossetti@unibocconi.it.

Titman, A. and Sharples, L.D. (2010). Model diagnostics for multi-state models, Statistical Methods in Medical Research, 19, 621-651.

Titman, A. and Sharples, L.D. (2008). A general goodness-of-fit test for Markov and hidden Markov models, Statistics in Medicine, 27, 2177-2195.

Gentleman RC, Lawless JF, Lindsey JC, Yan P. (1994). Multi-state Markov models for analysing incomplete disease data with illustrations for HIV disease. Statistics in Medicine, 13:805-821.

Jackson, C.H. (2011). Multi-State Models for Panel Data:
The msm Package for R. Journal of Statistical Software, 38(8), 1-29.
URL https://www.jstatsoft.org/v38/i08/.

plot.prevalence.msm msm prevalence.msm

## Not run: 
data( hosp )

# augmenting the data
hosp_augmented = augment( data = hosp, data_key = subj, n_events = adm_number,
                          pattern = label_3, t_start = dateIN, t_end = dateOUT,
                          t_cens = dateCENS )

# let's define the initial transition matrix for our model
Qmat = matrix( data = 0, nrow = 3, ncol = 3, byrow = TRUE )
Qmat[ 1, 1:3 ] = 1
Qmat[ 2, 1:3 ] = 1
colnames( Qmat ) = c( 'IN', 'OUT', 'DEAD' )
rownames( Qmat ) = c( 'IN', 'OUT', 'DEAD' )

# attaching the msm package and running the model using
# gender and age as covariates
library( msm )
msm_model = msm( status_num ~ augmented_int, subject = subj,
                 data = hosp_augmented, covariates = ~ gender + age,
                 exacttimes = TRUE, gen.inits = TRUE, qmatrix = Qmat,
                 method = 'BFGS', control = list( fnscale = 6e+05, trace = 0,
                 REPORT = 1, maxit = 10000 ) )

# defining the times at which compute the prevalences
t_min = min( hosp_augmented$augmented_int )
t_max = max( hosp_augmented$augmented_int )
steps = 100L

# computing prevalences
prev = prevalence.msm( msm_model, covariates = 'mean', ci = 'normal',
                       times = seq( t_min, t_max, steps ) )

# and plotting them using prevplot()
gof = prevplot( x = msm_model, prev.obj = prev, ci = TRUE, M = TRUE )

## End(Not run)