I am trying to generate predictions from a weibull survival curve but it
seems that the predictions assume that the shape(scale for
survfit) parameter is one(Exponential but with a strange rate estimate?).
Here is an examle of the problem, the smaller the shape is the worse the
discrepancy.
### Set Parameters
scale<-10
shape<-.85
### Find Mean
scale*gamma(1 + 1/shape)
### Simulate Data and Fit Model
y<-rweibull(10000,scale=scale,shape=shape)
model<-survreg(Surv(y)~1,dist="weibull")
### Exp of coef and predict are the same
exp(model$coef)
predict(model,type=c("response"))[1]
### Here is the mean and median of the data
mean(y)
median(y)
### Fitted Mean and Median from survreg
fitScale<-exp(model$coef)
fitShape<-1/model$scale
fitScale*gamma(1 + 1/fitShape)
fitScale*(log(2))^(1/fitShape)
Is this done on purpose? If so does anyone know why?
Thanks in advance,
Sean Brummel
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