This function summarizes both the stepwise selection process of the
model fitting by
oldlogspline, as well as the final model
that was selected using AIC/BIC. A
logspline object was fit using
the 1992 knot deletion algorithm (
The 1997 algorithm using knot
deletion and addition is available using the
1 2 3 4
other arguments are ignored.
These function produces the same 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.
Charles Kooperberg firstname.lastname@example.org.
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.
1 2 3
knots loglik AIC minimum penalty maximum penalty 3 -139.79 293.39 2.11 Inf 4 -139.78 297.98 NA NA 5 -139.05 301.13 NA NA 6 -137.28 302.19 NA NA 7 -135.56 303.36 0.27 2.11 8 -135.49 307.82 NA NA 9 -135.29 312.03 0.21 0.27 10 -135.19 316.43 0.06 0.21 11 -135.16 320.98 0.00 0.06 the present optimal number of knots is 3 penalty(AIC) was the default: BIC=log(samplesize): log( 100 )= 4.61
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.