ioprofile {ordinal}R Documentation

Produce Individual Ordinal Time Profiles for Plotting

Description

ioprofile is used for plotting individual ordinal profiles over time for objects obtained from dynamic models. It produces output for plotting recursive undelying means, recursive highest probabilities, and recursive cumulative probabilities for individual ordinal time profiles from such models.

See moprofile for plotting marginal ordinal profiles.

Usage

plot(ioprofile(z,curve.type="mean"),nind=1,observed=T,main=NULL,
     xlab=NULL,ylab=NULL,xlim=NULL,ylim=NULL,lty=NULL,pch=NULL,
     add=F,axes=F,bty="n",at=NULL,touch=F,...)

Arguments

z An object of class kalordinal, from kalord.
curve.type Specifies the type of curves to be plotted. Must either be "mean" for recursive underlying means, "probability" for recursive highest probabilities, "both" for recursive predictions (recursive underlying means and recursive highest probabilities), or "cumulative" for recursive cumulative probabilities.
nind Observation number(s) of individual(s) to be plotted.
observed If TRUE, adds the corresponding observations to the plot. If cumulative curves have been chosen, they are added as a subtitle.
main A main title for the plot.
xlab A label for the x-axis.
ylab A label for the y-axis.
xlim The x limits (min,max) of the plot.
ylim The y limits (min,max) of the plot.
lty A vector of integers or character strings specifying the line type to be used as the default in plotting lines. For further information, see par.
pch A vector of integers or single characters specifying symbols to be used as the default in plotting points. For further information, see par.
add If TRUE, the graph is added to an existing plot.
axes If FALSE, axes are not drawn around the plot.
bty A character string which determined the type of box which is drawn about plots. For further information, see par.
at The points at which tick-marks are to be drawn. For further information, see axis.
touch If TRUE, the x-axis and y-axis will touch each other.

Value

ioprofile returns information ready for plotting by plot.ioprofile.

Author(s)

P.J. Lindsey

See Also

kalord, moprofile, plot.ordinal, poprofile.

Examples

library(ordinal)

#
# Binary data
#
data(cardiac.indiv)

y <- restovec(cardiac.indiv[,1:4],type="ordinal")

cov <- tcctomat(as.matrix(cardiac.indiv[,5:10]))

w <- rmna(y,ccov=cov)

rm(cardiac.indiv,y,cov)

# Time-constant and time-varying covariate with a frailty dependence.
z <- kalord(w,distribution="binary",mu=~age+ren+cop+dia+sex+pmi+times,
            ptvc=c(4.43357,-0.03128,-0.62602,-0.37679,-0.32969,-0.17013,
                   -0.12209,-0.09095),pinit=0.1196,dep="frailty")

# Recursive mean profiles.
par(mfrow=c(2,2))
plot(ioprofile(z,"mean"),nind=1)
plot(ioprofile(z,"mean"),nind=5)
plot(ioprofile(z,"mean"),nind=c(1,5))
plot(ioprofile(z,"mean"),nind=12)
par(mfrow=c(1,1))

# Recursive highest probability profiles.
par(mfrow=c(2,2))
plot(ioprofile(z,"prob"),nind=1)
plot(ioprofile(z,"prob"),nind=5)
plot(ioprofile(z,"prob"),nind=c(1,5))
plot(ioprofile(z,"prob"),nind=12)
par(mfrow=c(1,1))

# Recursive predicted profiles.
par(mfrow=c(2,3))
plot(ioprofile(z,"both"),nind=1)
plot(ioprofile(z,"both"),nind=5)
plot(ioprofile(z,"both"),nind=12)
plot(ioprofile(z,"both"),nind=c(1,5,12),add=T)
par(mfrow=c(1,1))

# Recursive cumulative probability profiles.
par(mfrow=c(2,2))
plot(ioprofile(z,"cum"),nind=1)
plot(ioprofile(z,"cum"),nind=5)
plot(ioprofile(z,"cum"),nind=c(1,5),add=T)
par(mfrow=c(1,1))

rm(w,z)

#
# Ordinal data
#
data(tmi2)

y <- restovec(tmi2[,1:4],type="ordinal")

cov <- tcctomat(tmi2[,5],name="distance")

w <- rmna(y,ccov=cov)

rm(tmi2,y,cov)

# Proportional-odds model with time-constant covariate with a Markov dependence.
z <- kalord(w,distribution="proportional-odds",ccov=~distance,
            preg=c(-1.89,11.652,-0.199),pinit=3.111,pdep=0.217,dep="Markov")

# Recursive mean profiles.
par(mfrow=c(2,2))
plot(ioprofile(z,"mean"),nind=1)
plot(ioprofile(z,"mean"),nind=268)
plot(ioprofile(z,"mean"),nind=c(1,268))
plot(ioprofile(z,"mean"),nind=117)
par(mfrow=c(1,1))

# Recursive highest probability profiles.
par(mfrow=c(2,2))
plot(ioprofile(z,"prob"),nind=1)
plot(ioprofile(z,"prob"),nind=268)
plot(ioprofile(z,"prob"),nind=c(1,268))
plot(ioprofile(z,"prob"),nind=117)
par(mfrow=c(1,1))

# Recursive predicted profiles.
par(mfrow=c(2,2))
plot(ioprofile(z,"both"),nind=120)
plot(ioprofile(z,"both"),nind=268)
plot(ioprofile(z,"both"),nind=c(1,117),add=T)
par(mfrow=c(1,1))

# Recursive cumulative probability profiles.
par(mfrow=c(2,2))
plot(ioprofile(z,"cum"),nind=1)
plot(ioprofile(z,"cum"),nind=268)
plot(ioprofile(z,"cum"),nind=c(117,120),add=T)
par(mfrow=c(1,1))

rm(w,z)

[Package ordinal version 0.3 Index]