lines.TPmsm: lines method for a TPmsm object

In TPmsm: Estimation of Transition Probabilities in Multistate Models

lines method for a TPmsm object

Description

lines method for an object of class ‘TPmsm’.

Usage

## S3 method for class 'TPmsm'
lines(x, 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 ‘TPmsm’.
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. Default is black.
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

See Also

legend, lines, plot.default, plot.TPmsm.

Examples

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

# Create survTP object
data(bladderTP)
bladderTP_obj <- with( bladderTP, survTP(time1, event1, Stime, event) )

# Compute transition probabilities without confidence band
KMW <- transKMW(object=bladderTP_obj, s=5, t=59, conf=FALSE, method.est=1)
KMPW <- transKMPW(object=bladderTP_obj, s=5, t=59, conf=FALSE, method.est=1)
AJ <- transAJ(object=bladderTP_obj, s=5, t=59, conf=FALSE)
PAJ <- transPAJ(object=bladderTP_obj, s=5, t=59, conf=FALSE)
LIN <- transLIN(object=bladderTP_obj, s=5, t=59, conf=FALSE)
LS <- transLS(object=bladderTP_obj, s=5, t=59, h=c(0.25, 2.5),
nh=25, ncv=50, conf=FALSE)

# Plot '1 2' KMW transition probability estimate
par( mfrow=c(1, 1) )
plot(KMW, tr.choice="1 2", ylab="P12(5, Time)", xlab="Time",
col=1, lty=1, legend=FALSE)

# Add other '1 2' transition probability estimates
lines(KMPW, tr.choice="1 2", col=2, lty=1)
lines(AJ, tr.choice="1 2", col=3, lty=1)
lines(PAJ, tr.choice="1 2", col=4, lty=1)
lines(LIN, tr.choice="1 2", col=5, lty=1)
lines(LS, tr.choice="1 2", col=6, lty=1)

# Add legend
legend(x="topleft", legend=c("KMW", "KMPW", "AJ", "PAJ", "LIN", "LS"),
col=1:6, lty=1, bty="n")

# Plot all the transitions
tr.choice <- colnames(KMW$est)
par.orig <- par( c("mfrow", "cex") )
par( mfrow=c(2, 3) )
for ( i in seq_len( length(tr.choice) ) ) {
	plot(KMW, tr.choice=tr.choice[i], col=1, lty=1, legend=FALSE,
	main=tr.choice[i], xlab="", ylab="")
	lines(KMPW, tr.choice=tr.choice[i], col=2, lty=1)
	lines(AJ, tr.choice=tr.choice[i], col=3, lty=1)
	lines(PAJ, tr.choice=tr.choice[i], col=4, lty=1)
	lines(LIN, tr.choice=tr.choice[i], col=5, lty=1)
	lines(LS, tr.choice=tr.choice[i], col=6, lty=1)
}
plot.new()
legend(x="center", legend=c("KMW", "KMPW", "AJ", "PAJ", "LIN", "LS"),
col=1:6, lty=1, bty="n", cex=1.5)
par(mfrow=c(1, 1), cex=1.2)
title(xlab="Time", ylab="Transition probability", line=3)
par(par.orig)

# Restore the number of threads
setThreadsTP(nth)

TPmsm documentation built on Jan. 14, 2023, 1:17 a.m.