coxScoreHess: Calculate the Score vector / Hessian matrix for the Cox model

In statapps/lpl: Local Partial Likelihood Estimation and Simultaneous Confidence Band

Calculate the Score vector / Hessian matrix for the Cox model

Description

Calculate the Score vector or the Hessian matrix for the Cox proportional hazards model with inputs of covariates, survival outcomes and the relative risks

Usage

    coxScoreHess(X, y, exb, hess = FALSE)

Arguments

X	the covariate matrix from model.matrix, without the interecpt term.
y	y is a survival object, y = Surv(time, event).
exb	exb is the relative risks with exb = exp(X*beta).
hess	output the Hessian matrix, with hess = FALSE as the default, which outputs the score vector only,

Details

The survival time shall be sorted from the largest to the smallest, an error will occur if y is not sorted.

score = sum(event*(X - S1/S))

Sigma = sum(S1*t(S1))

H = sum(event*(S2/S0 - S1*t(S1)/S0))

the robust varaince can be calculated by inv(H)*Sigma*inv(H).

Value

An p by 1 vector of the score of the function calculated at the point relative exp(X*beta). If hess = TRUE, then a list with the following three components is returned:

score	a 1 x p score vector.
Sigma	a p x p matrix for the empirical varaince of the score.
H	a p x p hessian matrix.

See Also

numHessian numScore multiRoot

statapps/lpl documentation built on June 1, 2025, 6:58 p.m.