Last chance! 50% off unlimited learning
Sale ends in
iprofile
is used for plotting individual profiles over time
for objects obtained from dynamic models. It produces output for
plotting recursive fitted values for individual time profiles from
such models.
See mprofile
for plotting marginal profiles.
# S3 method for iprofile
plot(x, nind=1, observed=TRUE, intensity=FALSE,
add=FALSE, lty=NULL, pch=NULL, ylab=NULL, xlab=NULL,
main=NULL, ylim=NULL, xlim=NULL, ...)
iprofile
returns information ready for plotting by
plot.iprofile
.
An object of class iprofile
, e.g. x = iprofile(z, plotsd=FALSE)
,
where z
is an object of class recursive
, from
carma
, elliptic
,
gar
, kalcount
,
kalseries
, kalsurv
, or
nbkal
.
If plotsd
is If TRUE, plots standard deviations around profile
(carma
and elliptic
only).
Observation number(s) of individual(s) to be plotted.
If TRUE, plots observed responses.
If z has class, kalsurv
, and this is TRUE, the
intensity is plotted instead of the time between events.
If TRUE, the graph is added to an existing plot.
See base plot.
Arguments passed to other functions.
J.K. Lindsey
mprofile
plot.residuals
.
if (FALSE) {
## try this after you have repeated package installed
library(repeated)
times <- rep(1:20,2)
dose <- c(rep(2,20),rep(5,20))
mu <- function(p) exp(p[1]-p[3])*(dose/(exp(p[1])-exp(p[2]))*
(exp(-exp(p[2])*times)-exp(-exp(p[1])*times)))
shape <- function(p) exp(p[1]-p[2])*times*dose*exp(-exp(p[1])*times)
conc <- matrix(rgamma(40,1,scale=mu(log(c(1,0.3,0.2)))),ncol=20,byrow=TRUE)
conc[,2:20] <- conc[,2:20]+0.5*(conc[,1:19]-matrix(mu(log(c(1,0.3,0.2))),
ncol=20,byrow=TRUE)[,1:19])
conc <- ifelse(conc>0,conc,0.01)
z <- gar(conc, dist="gamma", times=1:20, mu=mu, shape=shape,
preg=log(c(1,0.4,0.1)), pdepend=0.5, pshape=log(c(1,0.2)))
# plot individual profiles and the average profile
plot(iprofile(z), nind=1:2, pch=c(1,20), lty=3:4)
plot(mprofile(z), nind=1:2, lty=1:2, add=TRUE)
}
Run the code above in your browser using DataLab