R: Produce Individual Time Profiles for Plotting

iprofile {rmutil} | R Documentation

## Produce Individual Time Profiles for Plotting

### Description

`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.

### Usage

    ## S3 method for class '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, ...)

### Arguments

`x` | 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).
`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.

`lty,pch,main,ylim,xlim,xlab,ylab` | See base plot.
`...` | Arguments passed to other functions.

### Value

`iprofile` returns information ready for plotting by `plot.iprofile`.

### Author(s)

J.K. Lindsey

### See Also

`mprofile` `plot.residuals`.

### Examples

    ## Not run:
    ## 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)

    ## End(Not run)

[Package rmutil version 1.1.3 Index]