proprate2.ks {surv2sample} | R Documentation |
Checks the assumption of proportional rates (proportional hazards, proportional odds) in two samples of right-censored data using the Kolmogorov–Smirnov test based on the simplified partial likelihood score process.
proprate2.ks(x, group, model = 0, nsim = 2000, nsim.plot = 50, beta.init = 0, maxiter = 20, eps = 1e-09)
x |
a "Surv" object, as returned by the Surv
function. |
group |
a vector indicating to which group each observation belongs. May contain values 1 and 2 only. |
model |
the type of model. Possible values are 0 for proportional hazards, 1 for proportional odds. |
nsim |
the number of simulations to approximate the p-value. Must be positive. |
nsim.plot |
the number of simulated paths of the test process
to be returned (for possible plotting). Must be at most nsim . |
beta.init |
the initial parameter value for iteration in the simplified partial likelihood estimation. |
maxiter |
the maximum number of iterations. |
eps |
the convergence tolerance parameter. The convergence criterion
is |(l-l_old)/l|<eps . |
This function tests the hypothesis that transformation rates (currently hazard rates or odds functions) are proportional (their ratio is constant in time) in two samples of censored survival data.
The proportional rate model is estimated by a two-sample simplification of the partial likelihood. The test then uses the Kolmogorov–Smirnov supremum statistic based on the simplified partial likelihood score process. The p-value is computed using the martingale simulation technique.
A list of class "proprate2.ks"
and "lwy.test"
,
with main components:
stat |
the test statistic. |
pval.sim |
the simulation based p-value. |
test.process |
the test process. |
test.process.sim |
simulated paths of the test process
(a matrix with nsim.plot columns). |
time |
sorted times. |
Some of input parameters and further components are included too.
David Kraus (http://www.davidkraus.net/)
Bagdonavicius, V. and Nikulin, M. (2000) On goodness-of-fit for the linear transformation and frailty models. Statist. Probab. Lett. 47, 177–188.
plot
method inherited from
the class "lwy.test"
proprate2.neyman
, proprate2.gs
for other
tests of the proportional rate assumption
proprate2
for estimation
## chronic active hepatitis data data(hepatitis) ## perform the Komogorov--Smirnov test of proportional odds a = proprate2.ks(Surv(hepatitis$time, hepatitis$status), hepatitis$treatment, model = 1) a ## plot the test process and simulated paths plot(a)