summary.survPen: Summary for a 'survPen' fit

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

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)

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