ehr {event} | R Documentation |
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 supplied as
formulae where parameters are unknowns. Factor variables cannot be
used and parameters must be scalars. (See finterp
.)
ehr(point, lambda=NULL, linear=NULL, plambda=NULL, delta=1, envir=parent.frame(), print.level=0, typsiz=rep(1,length(plambda)), ndigit=10, gradtol=0.00001, iterlim=100, fscale=1, stepmax=max(10*sqrt(plambda%*%plambda),10), steptol=0.0004)
point |
A point process vector produced by pp . |
lambda |
User-specified function of p , and possibly
linear , giving the regression equation for the intensity or a
formula beginning with ~, specifying either a linear regression
function in the Wilkinson and Rogers notation or a general function
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 estimates 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 . |
J.K. Lindsey
Lindsey, J.K. (1995) Fitting parametric counting processes by using log linear models. Journal of the Royal Statistical Society C44, 201-212.
bp
, finterp
,
ident
, pp
,
tccov
, tpast
,
ttime
, tvcov
.
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))