| poprofile {ordinal} | R Documentation |
poprofile is used for plotting predicted marginal ordinal
profiles over time for models obtained from dynamic models. These are
obtained from a function supplied by the user. It produces output for
plotting highest probabilities and cumulative probabilities for
predicted marginal ordinal profiles corresponding to a model fitted
with kalord.
See moprofile for plotting marginal ordinal profiles,
and ioprofile for plotting individual ordinal profiles.
plot(poprofile(mu,pintercept,preg,pinitial=NULL,depend="independence",
times=NULL,distribution="multinomial",
curve.type="probability"),main="Predicted profile",
xlab=NULL,ylab=NULL,xlim=NULL,ylim=NULL,lty=NULL,add=F,axes=F,
bty="n",at=NULL,touch=F,...)
mu |
The location regression as a function of the parameters and the times for the desired covariate values. The regression function must not contain intercepts. |
pintercept |
Intercept coefficients for the regression model. |
preg |
Remaining coefficients for the regression model: one for each time-constant and time-varying covariate, or one for each unknown parameter in their order of appearance. |
pinitial |
A coefficient for the initial parameter, if it is
NULL then this parameter will be fixed at zero. |
depend |
Type of dependence. Choices are independence
(default), Markov, serial, and frailty. |
times |
Vector of time points at which profiles are to be plotted. |
distribution |
Specifies the parameterization of the logistic distribution used in the Pareto distribution. Choices are binary, multinomial, continuation-ratio, and proportional-odds. |
curve.type |
Specifies the type of curves to be plotted. Must either be "probability" for highest probabilities or "cumulative" for cumulative probabilities. |
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. |
poprofile returns information ready for plotting by
plot.poprofile.
P.J. Lindsey
ioprofile, kalord,
moprofile, plot.ordinal.
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")
ren <- rep(0,4)
cop <- rep(0,4)
dia <- rep(0,4)
sex <- rep(0,4)
pmi <- rep(0,4)
times <- 1:4
# Predicted highest probability profiles.
par(mfrow=c(2,2))
for(i in c(1,25,50,80)) {
age <- rep(i,4)
mu <- finterp(attr(z$mu,"formula"),.intercep=F)
prof <- poprofile(mu,pint=4.43362,preg=c(4.43362,-0.03128,-0.62602,-0.37679,
-0.32969,-0.17013,-0.12209,-0.09095),
pinit=NULL,times=times,dist="binary",curve="prob")
plot(prof,main=paste("Age: ",i,sep=""))
}
par(mfrow=c(1,1))
# Predicted cumulative probability profiles.
par(mfrow=c(2,2))
for(i in c(1,25,50,80)) {
age <- rep(i,4)
mu <- finterp(attr(z$mu,"formula"),.intercep=F)
prof <- poprofile(mu,pint=4.43362,preg=c(-0.03128,-0.62602,-0.37679,-0.32969,
-0.17013,-0.12209,-0.09095),
pinit=NULL,times=times,dist="binary",curve="cum")
plot(prof,main=paste("Age: ",i,sep=""))
}
par(mfrow=c(1,1))
rm(w,z,ren,cop,dia,sex,pmi,times,mu,i,age,prof)
#
# 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")
mu <- function(p) rep(p[1]*distance,4)
# Predicted highest probability profiles.
par(mfrow=c(1,2))
for(distance in 1:2)
plot(poprofile(mu,pint=c(-1.89,11.655),preg=-0.199,pinit=NULL,times=1:4,
dist="prop",curve="prob"),main=paste("Distance: ",distance,sep=""))
par(mfrow=c(1,1))
# Predicted cumulative probability profiles.
par(mfrow=c(1,2))
for(distance in 1:2)
plot(poprofile(mu,pint=c(-1.89,11.655),preg=-0.199,pinit=NULL,times=1:4,
dist="prop",curve="cum"),main=paste("Distance: ",distance,sep=""))
par(mfrow=c(1,1))
rm(w,z,mu,distance)