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.
# S3 method for hare
summary(object, ...)
# S3 method for hare
print(x, ...)hare object, typically the result of hare.
other arguments are ignored.
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).
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.
# NOT RUN {
fit <- hare(testhare[,1], testhare[,2], testhare[,3:8])
summary(fit)
# }
Run the code above in your browser using DataLab