Description Usage Arguments Details Author(s) References See Also Examples
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 (oldlogspline
).
The 1997 algorithm using knot
deletion and addition is available using the logspline
function.
1 2 3 4 
object,x 

... 
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 loglikelihood 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. (NA
s 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.
logspline
,
oldlogspline
,
plot.oldlogspline
,
doldlogspline
,
poldlogspline
,
qoldlogspline
,
roldlogspline
.
1 2 3  y < rnorm(100)
fit < oldlogspline(y)
summary(fit)

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
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