polspline (version 1.0.4)

summary.logspline: Logspline Density Estimation

Description

This function summarizes both the stepwise selection process of the model fitting by logspline, as well as the final model that was selected using AIC/BIC. A logspline object was fit using the 1997 knot addition and deletion algorithm. The 1992 algorithm is available using the oldlogspline function.

Usage

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

Arguments

object,x

logspline object, typically the result of logspline

...

other arguments are ignored.

Details

These function produce identical printed output. The main body is a table with five columns: the first column is a possible number of knots for the fitted model;

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

the third column is -2 * loglikelihood + penalty * (number of knots - 1), which is the AIC criterion; logspline selected the model with the smallest value of AIC;

the fourth and fifth 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 is the value of penalty that was used.

References

Charles Kooperberg and Charles J. Stone. Logspline density estimation for censored data (1992). Journal of Computational and Graphical Statistics, 1, 301--328.

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

logspline, plot.logspline, dlogspline, plogspline, qlogspline, rlogspline, oldlogspline.

Examples

y <- rnorm(100)
fit <- logspline(y)       
summary(fit)