(Excess) hazard model with multidimensional penalized splines for given smoothing parameters
Fits an (excess) hazard model. If penalized splines are present, the smoothing parameters are specified.
survPen.fit( build, data, formula, max.it.beta = 200, beta.ini = NULL, detail.beta = FALSE, method = "LAML", tol.beta = 1e-04 )
build |
list of objects returned by |
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 |
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 |
Object of class "survPen" (see survPenObject for details)
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)
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