mprofile {rmutil}R Documentation

Produce Marginal Time Profiles for Plotting

Description

mprofile is used for plotting marginal profiles over time for models obtained from dynamic models, for given fixed values of covariates. These are either obtained from those supplied by the model, if available, or from a function supplied by the user.

See iprofile for plotting individual profiles from recursive fitted values.

Usage

zz <- mprofile(z, times=NULL, mu=NULL, ccov, plotse=TRUE)
plot(zz, nind=1, intensity=FALSE, add=FALSE, ylim=c(min(z$pred),max(z$pred)),
        lty=NULL, ylab="Fitted value", xlab="Time", ...)

Arguments

z An object of class recursive, from carma, elliptic, gar, kalcount, kalseries, kalsurv, or nbkal.
zz An object of class mprofile/
times Vector of time points at which profiles are to be plotted.
mu The location regression as a function of the parameters and the times for the desired covariate values.
ccov Covariate values for the profiles (carma only).
plotse If TRUE, plot standard errors (carma only).
nind Observation number(s) of individual(s) to be plotted. (Not used if mu is supplied.)
intensity If TRUE, the intensity is plotted instead of the time between events. Only for models produced by kalsurv.
add If TRUE, add contour to previous plot instead of creating a new one.
others Plotting control options.

Value

mprofile returns information ready for plotting by plot.mprofile.

Author(s)

J.K. Lindsey

See Also

carma, elliptic, gar, kalcount, kalseries, kalsurv, nbkal iprofile, plot.residuals.

Examples

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)

[Package rmutil version 1.0 Index]