uni.score {compound.Cox} | R Documentation |
Univariate significance analyses via the score tests (Witten & Tibshirani 2010; Emura et al. 2019) based on association between individual features and survival.
uni.score(t.vec, d.vec, X.mat, d0=0)
t.vec |
Vector of survival times (time to either death or censoring) |
d.vec |
Vector of censoring indicators, 1=death, 0=censoring |
X.mat |
n by p matrix of covariates, where n is the sample size and p is the number of covariates |
d0 |
A positive constant to stabilize the variance (Witten & Tibshirani 2010) |
score test
beta |
Estimated regression coefficients (one-step estimator) |
Z |
Z-value for testing H_0: beta=0 (score test) |
P |
P-value for testing H_0: beta=0 (score test) |
Takeshi Emura and Shigeyuki Matsui
Emura T, Matsui S, Chen HY (2018-). compound.Cox: Univariate Feature Selection and Compound Covariate for Predicting Survival, Computer Methods and Programs in Biomedicine, to appear.
Witten DM, Tibshirani R (2010) Survival analysis with high-dimensional covariates. Stat Method Med Res 19:29-51
data(Lung)
t.vec=Lung$t.vec[Lung$train==TRUE]
d.vec=Lung$d.vec[Lung$train==TRUE]
X.mat=Lung[Lung$train==TRUE,-c(1,2,3)]
uni.score(t.vec, d.vec, X.mat)