Hello again,
I studied your suggestion but still I disagree. You wrote:
"From the way you wrote the problem I assumed
that there is some number of n "looks" at the subject and then you count them
up."
But this is not the case. My data is clearly continuous quantities and no
discrete choices
Terry Therneau schrieb:
> Apologies -- you are being more subtle than I thought. Nevertheless, I think
> that the censoring language isn't quite right.
>
> You are thinking of a hierarchical model:
>
> z ~ N(Xb, sigma), where Xb is the linear predictor, whatever covariates
> you
> thin
--- Begin Message ---
Apologies -- you are being more subtle than I thought. Nevertheless, I think
that the censoring language isn't quite right.
You are thinking of a hierarchical model:
z ~ N(Xb, sigma), where Xb is the linear predictor, whatever covariates you
think belong in the m
> --begin included -
> My endogenous variable is not a time depending variable but percentages
> which naturally are censored in the interval [0,100]. Unfortunately many
> data points are 0 or 100 exactly. The rest of the data is asymmetrically
> distributed. So I would like to apply a two-li
Hello again,
thank you very much for your help so far.
To be more specific, I generate a simplified data set that is similar to
my real world data:
set.seed( 123 )
data <- data.frame( x = runif( 200 ), y = NA )
for( i in 1:200 ){
data$y[ i ] <- rweibull( 1, 1, 70 + 10 * data$x[ i ] ) - 30
}
d
Hello,
I have interval censored data, censored between (0, 100). I used the
tobit function in the AER package which in turn backs on survreg.
Actually I'm struggling with the distribution. Data is asymmetrically
distributed, so first choice would be a Weibull distribution.
Unfortunately the Wei
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