polspline (version 1.0.4)

summary.heft: Heft: hazard estimation with flexible tails

Description

This function summarizes both the stepwise selection process of the model fitting by heft, as well as the final model that was selected using AIC/BIC.

Usage

summary.heft(object, ...) 
print.heft(x, ...)

Arguments

object,x

heft object, typically the result of heft.

...

other arguments are ignored.

Details

These function produce identical printed output. The main body is a table with six columns:

the first column is a possible number of knots for the fitted model;

the second column is 0 if the model was fitted during the addition stage and 1 if the model was fitted during the deletion stage;

the third column is the log-likelihood for the fit;

the fourth column is -2 * loglikelihood + penalty * (dimension), which is the AIC criterion - heft selected the model with the minimum value of AIC;

the fifth and sixth columns give the endpoints of the interval of values of penalty that would yield the model with the indicated number of knots. (NAs imply that the model is not optimal for any choice of penalty.)

At the bottom of the table the number of knots corresponding to the selected model is reported, as are the value of penalty that was used and the coefficients of the log-based terms in the fitted model and their standard errors.

References

Charles Kooperberg, Charles J. Stone and Young K. Truong (1995). Hazard regression. Journal of the American Statistical Association, 90, 78-94.

Charles J. Stone, Mark Hansen, Charles Kooperberg, and Young K. Truong. The use of polynomial splines and their tensor products in extended linear modeling (with discussion) (1997). Annals of Statistics, 25, 1371--1470.

See Also

heft, plot.heft, dheft, hheft, pheft, qheft, rheft.

Examples

fit1 <- heft(testhare[,1], testhare[,2])
summary(fit1)
# modify tail behavior
fit2 <- heft(testhare[,1], testhare[,2], leftlog = FALSE, rightlog = FALSE, 
    leftlin = TRUE)   
summary(fit2)
fit3 <- heft(testhare[,1], testhare[,2], penalty = 0)   # select largest model
summary(fit3)