| Title: | Fast Regression for the Accelerated Failure Time (AFT) Model |
|---|---|
| Description: | Fast censored linear regression for the accelerated failure time (AFT) model of Huang (2013) <doi:10.1111/sjos.12031>. |
| Authors: | Yijian Huang <[email protected]> |
| Maintainer: | Yijian Huang <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 1.4 |
| Built: | 2024-12-16 06:49:31 UTC |
| Source: | CRAN |
An implementation of the fast censored linear regression in Huang (2013).
faft(x,dlt,z,weight="logrank",ynci=0,epl=0.95,epu=0.05)faft(x,dlt,z,weight="logrank",ynci=0,epl=0.95,epu=0.05)
x |
follow-up time. |
dlt |
censoring indicator: 1 - event, 0 - censored. |
z |
matrix of covariates: each column corresponds to a covariate. |
weight |
either "logrank" or "Gehan" estimating function. |
ynci |
compute test inversion-based 95% CI's? 1 - yes, 0 - no. |
epl |
parameter in (0,1) for determining the lower quantile from censored quantile regression (Huang 2010) for the preparatory estimation: sum of squared covariates for at-risk uncensored individuals is about $epl^(dim(z)[2]+1)$ in determinant. |
epu |
parameter in (0,1) for determining the upper quantile from censored quantile regression (Huang 2010) for the preparatory estimation: sum of squared covariates for at-risk uncensored individuals is about $epu^(dim(z)[2]+1)$ in determinant. |
weight |
either "logrank" or "Gehan" estimating function. |
beta |
estimated regression coefficient (the proposed). |
va |
sandwich variance estimate for beta. |
qif |
quadratic score statistic at beta. |
ci95 |
test inversion-based 95% CI's, only available if requested and successful. |
message |
point estimation: "success", "error - algorithm fails", or "warning - singular hessian". |
imsg |
numerical code for point and test inversion-based interval estimation: 0 - success in point and interval, 1 - error in point where algorithm fails, 2 - warning in point with singular hessian, 3 - success in point but failure in interval. |
beta1stp |
the one-step estimator. |
qif1stp |
quadratic score statistic at beta1stp. |
betainit |
the initial estimator. |
qifinit |
quadratic score statistic at betainit. |
Yijian Huang
Huang, Y. (2010) Quantile calculus and censored regression, The Annals of Statistics 38, 1607–1637.
Huang, Y. (2013) Fast censored linear regression. Scandinavian Journal of Statistics 40, 789–806.
## simulate a dataset of size 100 with 2 covariates size <- 100 npred <- 2 beta <- rep(1,npred) cvt <- matrix(rnorm(size*npred),ncol=npred) resid <- log(rexp(size)) event.t <- resid + cvt %*% beta censr.t <- log(runif(size, 0, 6)) x <- pmin(event.t, censr.t) dlt <- as.numeric(event.t<=censr.t) ## run censored linear regression fit.g <- faft(x,dlt,cvt,weight="Gehan") fit.l <- faft(x,dlt,cvt,weight="logrank")## simulate a dataset of size 100 with 2 covariates size <- 100 npred <- 2 beta <- rep(1,npred) cvt <- matrix(rnorm(size*npred),ncol=npred) resid <- log(rexp(size)) event.t <- resid + cvt %*% beta censr.t <- log(runif(size, 0, 6)) x <- pmin(event.t, censr.t) dlt <- as.numeric(event.t<=censr.t) ## run censored linear regression fit.g <- faft(x,dlt,cvt,weight="Gehan") fit.l <- faft(x,dlt,cvt,weight="logrank")