This function summarizes both the stepwise selection process of the
model fitting by hare, as well as the final model
that was selected using AIC/BIC.
Usage
## S3 method for class 'hare'
summary(object, ...)
## S3 method for class 'hare'
print(x, ...)
Arguments
object,x
hare object, typically the result of hare.
...
other arguments are ignored.
Details
These function produce identical printed output. The main body consists of
two tables.
The first table has six columns: the first column is a
possible number of dimensions for the fitted model;
the second column indicates whether this model was fitted during
the addition or 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 - hare selected the model with
the minimum value of AIC;
the last two columns give the
endpoints of the interval of values of penalty that would yield the
model with the indicated number of dimensions
(NAs imply that the model is not optimal for any choice of penalty).
At the bottom of the first table the
dimension of the selected model is reported, as is
the value of penalty that was used.
Each row of the second table summarizes the information about
a basis function in
the final model. It shows the variables involved, the knot locations, the
estimated coefficient and its standard error and Wald statistic (estimate/SE).
Note
Since the basis functions are selected in an adaptive fashion, typically
most Wald statistics are larger than (the magical) 2. These statistics
should be taken with a grain of salt though, as they are inflated because
of the adaptivity of the model selection.
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.