# mprofile: Produce Marginal Time Profiles for Plotting In rmutil: Utilities for Nonlinear Regression and Repeated Measurements Models

## 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

 ```1 2 3``` ```## 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 `z`An 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

`iprofile`, `plot.residuals`.
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19``` ```## 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) ```