coxr | R Documentation |
Fits efficiently and robustly Cox proportional hazards regression model in its basic form, where explanatory variables are time independent with one event per subject. Method is based on a smooth modification of the partial likelihood.
coxr( formula, data, subset, na.action, trunc = 0.95, f.weight = c("linear", "quadratic", "exponential"), singular.ok = TRUE, model = FALSE )
formula |
a formula object, with the response on the left of a |
data |
a data frame in which to interpret the variables
named in the |
subset |
expression saying that only a subset of the rows of the data should be used in the fit. |
na.action |
a missing-data filter function, applied to the model.frame, after any subset argument has been used. |
trunc |
roughly, quantile of the sample T_i exp(β'Z_i), it determines the trimming level for the robust estimator. |
f.weight |
type of weighting function, default is |
singular.ok |
logical value indicating how to handle collinearity in the
model matrix. If |
model |
a logical value indicating whether model frame should be included as a component of the returned value. |
a data frame containing MCMC summary statistics.An object of class
coxr
. See coxr.object
for details.
Bednarski, T. (1993). Robust estimation in Cox's regression model. Scandinavian Journal of Statistics. Vol. 20, 213–225.
Bednarski, T. (1989). On sensitivity of Cox's estimator. Statistics and Decisions. 7, 215–228.
Grzegorek, K.(1993). On robust estimation of baseline hazard under the Cox model and via Frechet differentiability. Preprint of the Institute of Mathematics of the Polish Academy of Sciences.518.
Minder, C.E. & Bednarski, T. (1996). A robust method for proportional hazards regression. Statistics in Medicine Vol. 15, 1033–1047.
if (interactive()) { # Create a simple test data set using the attached function gen_data a <- gen_data(200, c(1, 0.1, 2), cont = 0.05, p.censor = 0.30) result <- coxr(Surv(time, status) ~ X1 + X2 + X3, data = a , trunc = 0.9) result plot(result) }
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