| iprofile(rmutil) | R Documentation |
plot(iprofile(z, plotsd=FALSE), 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. |
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 is used for plotting individual profiles over time
for models obtained from Kalman fitting. See profile for
plotting marginal profiles.
J.K. Lindsey
carma, elliptic, gar,
kalcount, kalseries,
kalsurv, nbkal profile
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,mu(log(c(1,0.3,0.2)))),ncol=20,byrow=T) conc[,2:20] <- conc[,2:20]+0.5*(conc[,1:19]-matrix(mu(log(c(1,0.3,0.2))), ncol=20,byrow=T)[,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(profile(z), nind=1:2, lty=1:2, add=T)