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 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
--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-limit tobit, r
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
The problem is that you are not coding your data the way that I would;
program
authors do not always anticipate what others will do! The Weibull distribution
has support on (0, infinity). Using Surv(t1, t2, type='interval2'), you can
have
a left censored observation where time of even
On Tue, 23 Dec 2008, Geraldine Henningsen wrote:
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
Surv() allows left, right, or interval censoring.
Try left censoring instead of interval censoring. For the weibull or
lognormal, think of your data as <=100 instead of [0,100].
-Don
At 8:08 PM +0100 12/23/08, Geraldine Henningsen wrote:
Hello,
I have interval censored data, censored betwee
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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