basehaz.gbm: Baseline hazard function

In gbm: Generalized Boosted Regression Models

Baseline hazard function

Description

Computes the Breslow estimator of the baseline hazard function for a proportional hazard regression model.

Usage

basehaz.gbm(t, delta, f.x, t.eval = NULL, smooth = FALSE, cumulative = TRUE)

Arguments

t	The survival times.
delta	The censoring indicator.
f.x	The predicted values of the regression model on the log hazard scale.
t.eval	Values at which the baseline hazard will be evaluated.
smooth	If TRUE basehaz.gbm will smooth the estimated baseline hazard using Friedman's super smoother supsmu.
cumulative	If TRUE the cumulative survival function will be computed.

Details

The proportional hazard model assumes h(t|x)=lambda(t)*exp(f(x)). gbm can estimate the f(x) component via partial likelihood. After estimating f(x), basehaz.gbm can compute the a nonparametric estimate of lambda(t).

Value

A vector of length equal to the length of t (or of length t.eval if t.eval is not NULL) containing the baseline hazard evaluated at t (or at t.eval if t.eval is not NULL). If cumulative is set to TRUE then the returned vector evaluates the cumulative hazard function at those values.

Author(s)

Greg Ridgeway [e-mail]

References

N. Breslow (1972). "Discussion of 'Regression Models and Life-Tables' by D.R. Cox," Journal of the Royal Statistical Society, Series B, 34(2):216-217.

N. Breslow (1974). "Covariance analysis of censored survival data," Biometrics 30:89-99.

See Also

survfit, gbm

gbm documentation built on June 28, 2024, 9:07 a.m.