mprofile {rmutil} | R Documentation |
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.
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", ...)
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. |
mprofile
returns information ready for plotting by
plot.mprofile
.
J.K. Lindsey
carma
, elliptic
,
gar
, kalcount
,
kalseries
, kalsurv
,
nbkal
iprofile
,
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,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)