summary.survPen {survPen}

Summary for a survPen fit

Description

Takes a fitted survPen object and produces various useful summaries from it.

Usage

## S3 method for class 'survPen'
summary(object, ...)

Arguments

object

a fitted survPen object as produced by survPen.fit

...

other arguments

Value

List of objects:

call

the original survPen call

formula

the original survPen formula

coefficients

reports the regression parameters estimates for unpenalized terms with the associated standard errors

edf.per.smooth

reports the edf associated with each smooth term

random

TRUE if there are random effects in the model

random.effects

reports the estimates of the log standard deviation (log(sd)) of every random effects plus the estimated standard error (also on the log(sd) scale)

likelihood

unpenalized likelihood of the model

penalized.likelihood

penalized likelihood of the model

nb.smooth

number of smoothing parameters

smoothing.parameter

smoothing parameters estimates

parameters

number of regression parameters

edf

effective degrees of freedom

method

smoothing selection criterion used (LAML or LCV)

val.criterion

minimized value of criterion. For LAML, what is reported is the negative log marginal likelihood

converged

convergence indicator, TRUE or FALSE. TRUE if Hess.beta.modif=FALSE and Hess.rho.modif=FALSE (or NULL)

Examples

library(survPen)

data(datCancer) # simulated dataset with 2000 individuals diagnosed with cervical cancer

# model : unidimensional penalized spline for time since diagnosis with 5 knots
f1 <- ~smf(fu,df=5)

# fitting hazard model
mod1 <- survPen(f1,data=datCancer,t1=fu,event=dead,expected=NULL,method="LAML")

# summary
summary(mod1)