

ehr(event)                                   R Documentation

_F_i_t _a_n _I_n_t_e_n_s_i_t_y _F_u_n_c_t_i_o_n _t_o _E_v_e_n_t _H_i_s_t_o_r_i_e_s

_D_e_s_c_r_i_p_t_i_o_n_:

     `ehr' fits an intensity function to event histories,
     where point is produced by `point <- pp(y)' and
     `lambda' is the user-defined log intensity function.

     Nonlinear regression models for `lambda' can be sup-
     plied as formulae where parameters are unknowns. Factor
     variables cannot be used and parameters must be
     scalars. (See `finterp'.)

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

     ehr(point, lambda=NULL, linear=NULL, plambda=NULL, delta=1,
             envir=sys.frame(sys.parent()), print.level=0,
             typsiz=rep(1,length(p)), ndigit=10, gradtol=0.00001,
             stepmax=max(10*sqrt(p%*%p),10), steptl=0.0004,
             iterlim=100, fscale=1)

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

   point: A point process vector produced by `pp'.

  lambda: User-specified function of `p', and possibly `lin-
          ear', giving the regression equation for the
          intensity or a formula beginning with ~, specify-
          ing either a linear regression function in the
          Wilkinson and Rogers notation or a general func-
          tion with named unknown parameters. The function
          may contain a linear part that must simply be
          given the name, `linear', in the function. If no
          function is supplied, the intensity is taken to be
          constant (a homogeneous Poisson process).

  linear: A formula beginning with ~ specifying the linear
          part of the regression function.

 plambda: Vector of initial parameter estimates. If `lambda'
          is a formula with unknown parameters, their esti-
          mates must be supplied either in their order of
          appearance in the expression or in a named list.

   delta: If any time intervals are different from unity, a
          vector of time intervals.

   envir: Environment in which model formulae are to be
          interpreted or a data object of class, repeated,
          tccov, or tvcov.  If `point' has class `repeated',
          it is used as the environment.

  others: Arguments controlling `nlm'.

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

     J.K. Lindsey

_R_e_f_e_r_e_n_c_e_s_:

     Lindsey, J.K. (1995) Fitting parametric counting pro-
     cesses by using log linear models. Journal of the Royal
     Statistical Society C44, 201-212.

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

     `bp', `finterp', `ident', `pp', `tccov', `tpast',
     `ttime', `tvcov'.

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

     y <- c(5,3,2,4)
     # event indicator
     py <- pp(y)
     # time since previous event
     ptime <- tpast(y)
     # individual ID
     i <- c(1,1,2,2)
     id <- ident(y, i)
     # times and corresponding covariate values
     tx <- c(2,3,1,2,2,2,2)
     x <- c(1,2,2,1,2,2,1)
     zcov <- tvcov(y, x, tx)
     # Poisson process
     ehr(py, plambda=1)
     # Weibull process
     lambda1 <- function(p) p[1]+p[2]*log(ptime)
     ehr(py, lambda=lambda1, plambda=c(1,1))
     # or
     ehr(py, lambda=~log(ptime), plambda=c(1,1))
     # or
     ehr(py, lambda=~b0+b1*log(ptime), plambda=list(b0=1,b1=1))
     # Poisson process with time-varying covariate
     lambda2 <- function(p) p[1]+p[2]*zcov
     ehr(py, lambda=lambda2, plambda=c(1,1))
     # or
     ehr(py, lambda=~zcov, plambda=c(1,1))
     # or
     ehr(py, lambda=~c0+c1*zcov, plambda=list(c0=1,c1=1))
     # Weibull process with time-varying covariate
     lambda3 <- function(p) p[1]+p[2]*log(ptime)+p[3]*zcov
     ehr(py, lambda=lambda3, plambda=c(1,1,1))
     # or
     ehr(py, lambda=~log(ptime)+zcov, plambda=c(1,1,1))
     # or
     ehr(py, lambda=~c0+b1*log(ptime)+c1*zcov, plambda=list(c0=1,c1=1,b1=1))
     # gamma process with time-varying covariate
     lambda4 <- function(p) hgamma(ptime, p[1], exp(p[2]+p[3]*zcov))
     ehr(py, lambda=lambda4, plambda=c(1,1,1))
     # or
     ehr(py, lambda=~hgamma(ptime, b1, exp(c0+c1*zcov)),
             plambda=list(c0=1,c1=1,b1=1))
     # or
     lambda5 <- function(p, linear) hgamma(ptime, p[1], exp(linear))
     ehr(py, lambda=lambda5, linear=~zcov, plambda=c(1,1,1))

