I agree with David that poisson regression would be the simplest thing.
It's a consequence of the poison formulation and an exponential "trick"
E(#breaks) = breaks per meter * length in meters
= exp(Xb) * exp(log(length))
= exp(Xb + log(length))
X = covariates that affe
I think you are replying to Dr Therneau without including this context:
--- begin
Survreg produces MLE estimates.
For your second question, don't know what you are asking. Can you be
more specific and detailed?
---begin included message --
Do you know if the parameters estimators are MLE
Sorry when we talk about about MLE estimates does that mean WLE?I am trying
to understand if the survreg function is allowing a weight for each density
function when calculating the likelihood.
In my second question I was trying to explain that my problem is that I have
pipes of different length a
Survreg produces MLE estimates.
For your second question, don't know what you are asking. Can you be
more specific and detailed?
---begin included message --
Do you know if the parameters estimators are MLE estimators?
One more question:
In my case study I have failures that occured on differ
Thank you for your reply, it has been helpful.
Do you know if the parameters estimators are MLE estimators?
One more question:
In my case study I have failures that occured on different objects that have
different age and length, could I use weight to find the estimates of a
weibull law and so to
The survreg function uses case weights. That is, if a subject is given
a weight of 2, the result is the same as if there were a second
observation (exactly the same).
Early in my career data sets that contained only categorical variables
were often collapsed in just this way, in order to save on
Dear R users,
I have been trying to understand what the Weights arguments is doing in the
estimation of the parameters when using the Surreg function.
I looked through the function's code but I am not sure if I got it or not.
For example, if I inclue the Surv function in it:
survreg(Surv(vector,
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