| iprofile {rmutil} | R Documentation |
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.
zz <- iprofile(z, plotsd=FALSE)
plot(zz, nind=1, observed=TRUE, intensity=F,
add=FALSE, lty=NULL, pch=NULL, ylab=NULL, xlab=NULL,
main=NULL, ylim=NULL, xlim=NULL, ...)
z |
An object of class recursive, from
carma, elliptic,
gar, kalcount,
kalseries, kalsurv, or
nbkal. |
zz |
An object of class iprofile. |
plotsd |
If TRUE, plots standard deviations around profile
(carma and elliptic only). |
nind |
Observation number(s) of individual(s) to be plotted. |
observed |
If TRUE, plots observed responses. |
intensity |
If z has class, kalsurv, and this is TRUE, the
intensity is plotted instead of the time between events. |
add |
If TRUE, the graph is added to an existing plot. |
others |
Plotting control options. |
iprofile returns information ready for plotting by
plot.iprofile.
J.K. Lindsey
carma, elliptic,
gar, kalcount,
kalseries, kalsurv,
nbkal mprofile
plot.residuals.
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)