moprofile {ordinal} | R Documentation |
mprofile
is used for plotting marginal ordinal profiles over
time for for objects obtained from models obtained. It produces output
for plotting highest probabilities and cumulative probabilities for
marginal ordinal time profiles.
See iprofile
for plotting individual ordinal profiles
from recursive fitted values.
plot(moprofile(z,curve.type="probability"),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,...)
z |
An object of class lcr or kalordinal
(kalord ). |
curve.type |
Specifies the type of curves to be plotted. Must either be "probability" for highest probabilities or "cumulative" for 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. |
moprofile
returns information ready for plotting by
plot.moprofile
.
P.J. Lindsey
kalord
, ioprofile
, lcr
,
plot.ordinal
, poprofile
.
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") # Cumulative probability profiles. par(mfrow=c(2,2)) plot(moprofile(z,"cum"),nind=1) plot(moprofile(z,"cum"),nind=117) plot(moprofile(z,"cum"),nind=c(1000,3000),add=T) par(mfrow=c(1,1)) # Highest probability profiles. par(mfrow=c(2,2)) plot(moprofile(z,"prob"),nind=2000) plot(moprofile(z,"prob"),nind=3001) plot(moprofile(z,"prob"),nind=c(3506,3521)) plot(moprofile(z,"prob"),nind=400) par(mfrow=c(1,1)) rm(w,z) # # Ordinal data # data(obese) resp <- cbind(codes(obese[,1])-1,codes(obese[,2])-1) freq <- obese[,4] age <- obese[,3] rm(obese) y <- restovec(resp,times=1:2,weights=freq,type="ordinal") tcc <- tcctomat(age,name="age") tvc <- tvctomat(matrix(times(y)^2,ncol=2),name="times2") w <- rmna(y,ccov=tcc,tvcov=tvc) rm(resp,freq,age,y,tcc,tvc) z <- lcr(w,mu=~age*times+times2) # Cumulative probability profiles. par(mfrow=c(2,2)) plot(moprofile(z,"cum"),nind=1) plot(moprofile(z,"cum"),nind=4) plot(moprofile(z,"cum"),nind=8:9,add=T) par(mfrow=c(1,1)) # Highest probability profiles. par(mfrow=c(2,2)) plot(moprofile(z,"prob"),nind=1) plot(moprofile(z,"prob"),nind=4) plot(moprofile(z,"prob"),nind=c(8,9)) plot(moprofile(z,"prob"),nind=16) par(mfrow=c(1,1)) rm(w,z)