lines.TPCmsm {TPmsm}

lines method for a TPCmsm object

Description

lines method for an object of class ‘TPCmsm’.

Usage

## S3 method for class 'TPCmsm'
lines(x, plot.type="t", tr.choice, col, lty, conf.int=FALSE,
ci.col, ci.lty, legend=FALSE, legend.pos, curvlab, legend.bty="n", ...)

Arguments

x

An object of class ‘TPCmsm’.

plot.type

A character string specifying the type of plot. If “t” the scatterplot of transition probability versus time is plotted. If “c” the scatterplot of transition probability versus covariate is plotted.

tr.choice

Character vector of the form ‘c(“from to”, “from to”)’ specifying which transitions should be plotted. Default, all the transition probabilities are plotted.

col

Vector of colour.

lty

Vector of line type. Default is 1:number of transitions.

conf.int

Logical. Whether to display pointwise confidence bands. Default is FALSE.

ci.col

Colour of the confidence bands. Default is col.

ci.lty

Line type of the confidence bands. Default is 3.

legend

A logical specifying if a legend should be added.

legend.pos

A vector giving the legend's position. See legend for further details.

curvlab

A character or expression vector to appear in the legend. Default is the name of the transitions.

legend.bty

Box type for the legend. By default no box is drawn.

...

Further arguments for lines.

Value

No value is returned.

Author(s)

Artur Araújo, Javier Roca-Pardiñas and Luís Meira-Machado

References

Araújo A, Meira-Machado L, Roca-Pardiñas J (2014). TPmsm: Estimation of the Transition Probabilities in 3-State Models. Journal of Statistical Software, 62(4), 1-29. doi:10.18637/jss.v062.i04

Meira-Machado L., de Uña-Álvarez J., Datta S. (2011). Conditional Transition Probabilities in a non-Markov Illness-death Model. Discussion Papers in Statistics and Operation Research n 11/03. Department of Statistics and Operations Research, University of Vigo (ISSN: 1888-5756, Deposito Legal VG 1402-2007). https://depc05.webs.uvigo.es/reports/12_05.pdf

See Also

legend, lines, plot.default, plot.TPCmsm.

Examples

# Set the number of threads
nth <- setThreadsTP(2);

# Create survTP object
data(heartTP);
heartTP_obj <- with( heartTP, survTP(time1, event1, Stime, event, age=age) );

# Compute IPCW1 conditional transition probabilities without confidence band
TPC_IPCW1 <- transIPCW(heartTP_obj, s=57, t=310, x=15, conf=FALSE, method.est=1);

# Compute IPCW2 conditional transition probabilities without confidence band
TPC_IPCW2 <- transIPCW(heartTP_obj, s=57, t=310, x=15, conf=FALSE, method.est=2);

# Compute LIN conditional transition probabilities without confidence band
TPC_LIN <- transLIN(heartTP_obj, s=57, t=310, x=15, conf=FALSE);

# Build covariate plots
tr.choice <- dimnames(TPC_LIN$est)[[3]];
par.orig <- par( c("mfrow", "cex") );
par( mfrow=c(2,3) );
for ( i in seq_len( length(tr.choice) ) ) {
	plot(TPC_IPCW1, plot.type="c", tr.choice=tr.choice[i], legend=FALSE,
	main=tr.choice[i], col=1, lty=1, xlab="", ylab="");
	lines(TPC_IPCW2, plot.type="c", tr.choice=tr.choice[i], legend=FALSE, col=2, lty=1);
	lines(TPC_LIN, plot.type="c", tr.choice=tr.choice[i], legend=FALSE, col=3, lty=1);
}
plot.new();
legend(x="center", legend=c("IPCW1", "IPCW2", "LIN"), col=1:3, lty=1, bty="n", cex=1.5);
par(mfrow=c(1, 1), cex=1.2);
title(xlab="Age", ylab="Transition probability", line=3);
par(par.orig);

# Restore the number of threads
setThreadsTP(nth);