

profile(rmutil)                              R Documentation

_P_r_o_d_u_c_e _M_a_r_g_i_n_a_l _T_i_m_e _P_r_o_f_i_l_e_s _f_o_r _P_l_o_t_t_i_n_g

_U_s_a_g_e_:

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

_A_r_g_u_m_e_n_t_s_:

       z: An object of class recursive, from `carma',
          `elliptic', `gar', `kalcount', `kalseries',
          `kalsurv', or `nbkal'.

   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 covari-
          ate values.

    ccov: Covariate values for the profiles (`carma' only).

  plotse: Plot standard errors (`carma' only).

    nind: Observation number(s) of individual(s) to be plot-
          ted. (Not used if `mu' is supplied.)

intensity: If z has class, `kalsurv', and this is TRUE, the
          intensity is plotted instead of the time between
          events.

     add: If TRUE, add contour to previous plot instead of
          creating a new one.

  others: Plotting control options.

_V_a_l_u_e_:

     `profile' is used for plotting marginal profiles over
     time for models obtained from Kalman fitting, for given
     fixed values of covariates. See `iprofile' for plotting
     individual profiles.

_A_u_t_h_o_r_(_s_)_:

     J.K. Lindsey

_S_e_e _A_l_s_o_:

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

_E_x_a_m_p_l_e_s_:

     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,mu(log(c(1,0.3,0.2)))),ncol=20,byrow=T)
     conc[,2:20] <- conc[,2:20]+0.5*(conc[,1:19]-matrix(mu(log(c(1,0.3,0.2))),
             ncol=20,byrow=T)[,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(profile(z), nind=1:2, lty=1:2, add=T)

