lines.TPmsm: lines method for a TPmsm object

View source: R/lines.TPmsm.R

lines.TPmsmR Documentation

lines method for a TPmsm object


lines method for an object of class ‘TPmsm’.


## S3 method for class 'TPmsm'
lines(x, tr.choice, col, lty,, ci.col, ci.lty,
legend=FALSE, legend.pos, curvlab, legend.bty="n", ...)



An object of class ‘TPmsm’.


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


Vector of colour. Default is black.


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

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


Colour of the confidence bands. Default is col.


Line type of the confidence bands. Default is 3.


A logical specifying if a legend should be added.


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


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


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


Further arguments for lines.


No value is returned.


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


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. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.18637/jss.v062.i04")}

See Also

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


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

# Create survTP object
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);
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);

# Restore the number of threads

TPmsm documentation built on May 29, 2024, 10:43 a.m.