On Thu, 9 Jun 2005 [EMAIL PROTECTED] wrote:
Quoting Prof Brian Ripley <[EMAIL PROTECTED]>:
On Thu, 9 Jun 2005 [EMAIL PROTECTED] wrote:
He,
I used the "coxph" function, with four covariates.
Let's say something like that
model.1 <- coxph(Surv(Time,Event)~X1+X2+X3+X4,data=DATA)
So I obtain the 4 coefficients B1,B2,B3,B4 such that
h(t) = h0(t) exp(B1*X1+ B2*X2 + B3*X3 + B4*X4).
When I use the function on the same data
predict.coxph(model.1,type="lp")
How does that work? predict.coxph is not an exported function!
how it works in making the prediction?
I mean which is the formula, given the data-point
P1=[X1(1),X2(1),X3(1),X4(1)],
that the function "predict.coxph" use to make the prediction of P1.
From the code (getAnywhere("predict.coxph"))
if (type == "lp" || type == "risk") {
if (missing(newdata)) {
pred <- object$linear.predictors
names(pred) <- names(object$residuals)
}
else pred <- x %*% coef + offset
...
so that is the formula it uses. As you did not supply 'newdata', it quotes
the 'linear.predictors' component of the fit: see ?coxph.object.
Effectively it centred the explanatory variables on their means and then
applied the linear regression formula to give the linear predictor. It is
the centring that may be non-obvious: effectively h_0(t), the baseline
hazard, is taken at the average of the subjects.
Dear Prof. Ripley
Thanks for replying to me email.
I only have an other question:
since h(t) = h0(t) exp(B1*X1+ B2*X2 + B3*X3 + B4*X4)
represent the hazard at time t.
In a linear prediction,
what Value = B1*(X1-mean(X1)) + B2*(X2-mean(X2)) + ....
represent?
The linear predictor, as you asked for.
--
Brian D. Ripley, [EMAIL PROTECTED]
Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/
University of Oxford, Tel: +44 1865 272861 (self)
1 South Parks Road, +44 1865 272866 (PA)
Oxford OX1 3TG, UK Fax: +44 1865 272595
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