| Library: | hazreg |
| See also: | harefit plothare xhare |
| Quantlet: | haresummary | |
| Description: | summarizes the output of harefit (non-proportional hazards regression). |
| Usage: | sum = haresummary(fit) | |
| Input: | ||
| fit | a list like the output from harefit. | |
| Output: | ||
| sum | This function produces an array of strings - 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.fit 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 (NaNs 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). | |
library("hazreg")
randomize(111)
n = 500
p = 2
beta = 1|2 ; regression parameter
z = 1 + uniform(n,p) ; covariates
y = -log(1-uniform(n)) ; exponential survival
y = y./exp(z*beta) ; covariate effects
c = 4*uniform(n) ; uniform censoring
t = min(y~c,2) ; censored time
delta =(y<=c) ; censoring indicator
fit=harefit(t,delta,z)
haresummary(fit)
Contents of sum [ 1,] "dim A/D loglik AIC penalty " [ 2,] " min max" [ 3,] " 0 Add -1.6e-46 -9.2e+102 1.4e+02 1.4e+02" [ 4,] " 0 Add 5.6e+175 -5.9e-144 42 42" [ 5,] " 0 Add 9.7e+43 -6.2e-127 1.3 1.3" [ 6,] " 0 Add -7.3e-252 2.7e-273 0.00" [ 7,] " " [ 8,] "the present optimal number of dimensions is 3." [ 9,] "penalty(AIC) was the default: BIC=log(samplesize): log(500)=6.2" [10,] " " [11,] " dim1 dim2 beta SE Wald" [12,] "Constant 0.21 0.32 0.64" [13,] "Co-2 linear 1.8 0.16 11" [14,] "Co-1 linear 1 0.16 6.5"