mprofile {rmutil}

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

## S3 method for class 'mprofile'
plot(x, nind=1, intensity=FALSE, add=FALSE, ylim=range(z$pred, na.rm = TRUE),
	lty=NULL, ylab=NULL, xlab=NULL, ...)

Arguments

x

An object of class mprofile, e.g. x = mprofile(z, times=NULL, mu=NULL, ccov, plotse=TRUE), where zAn object of class recursive, from carma, elliptic, gar, kalcount, kalseries, kalsurv, or nbkal; times is a vector of time points at which profiles are to be plotted; mu is the location regression as a function of the parameters and the times for the desired covariate values; ccov is covariate values for the profiles (carma only); and plotse when TRUE plots 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.

lty, ylim, xlab, ylab

See base plot.

...

Arguments passed to other functions.

Value

mprofile returns information ready for plotting by plot.mprofile.

Author(s)

J.K. Lindsey

See Also

iprofile, plot.residuals.

Examples

## Not run: 
## try after you get the repeated package
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)

## End(Not run)