Logspline Density Estimation
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.
## S3 method for class 'logspline' summary(object, ...) ## S3 method for class 'logspline' print(x, ...)
object,x |
|
... |
other arguments are ignored. |
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.
Charles Kooperberg clk@fredhutch.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.
y <- rnorm(100) fit <- logspline(y) summary(fit)
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