survPen.fit: (Excess) hazard model with multidimensional penalized splines...

In survPen: Multidimensional Penalized Splines for Survival and Net Survival Models

(Excess) hazard model with multidimensional penalized splines for given smoothing parameters

Description

Fits an (excess) hazard model. If penalized splines are present, the smoothing parameters are specified.

Usage

survPen.fit(
  build,
  data,
  formula,
  max.it.beta = 200,
  beta.ini = NULL,
  detail.beta = FALSE,
  method = "LAML",
  tol.beta = 1e-04
)

Arguments

build	list of objects returned by model.cons
data	an optional data frame containing the variables in the model
formula	formula object specifying the model
max.it.beta	maximum number of iterations to reach convergence in the regression parameters; default is 200
beta.ini	vector of initial regression parameters; default is NULL, in which case the first beta will be log(sum(event)/sum(t1)) and the others will be zero (except if there are "by" variables or if there is a piecewise constant hazard specification in which cases all betas are set to zero)
detail.beta	if TRUE, details concerning the optimization process in the regression parameters are displayed; default is FALSE
method	criterion used to select the smoothing parameters. Should be "LAML" or "LCV"; default is "LAML"
tol.beta	convergence tolerance for regression parameters; default is 1e-04. See NR.beta for details

Value

Object of class "survPen" (see survPenObject for details)

Examples

library(survPen)

# standard spline of time with 4 knots

data <- data.frame(time=seq(0,5,length=100),event=1,t0=0)

form <- ~ smf(time,knots=c(0,1,3,5))

t1 <- eval(substitute(time), data)
t0 <- eval(substitute(t0), data)
event <- eval(substitute(event), data)
	
# Setting up the model before fitting
model.c <- model.cons(form,lambda=0,data.spec=data,t1=t1,t1.name="time",
t0=rep(0,100),t0.name="t0",event=event,event.name="event",
expected=NULL,expected.name=NULL,type="overall",n.legendre=20,
cl="survPen(form,data,t1=time,event=event)",beta.ini=NULL)
 
# fitting
mod <- survPen.fit(model.c,data,form)

survPen documentation built on Sept. 14, 2023, 1:06 a.m.