PLEASE EXCUSE: This discussion has diverged from R into discussing the
precise assumptions seemingly descriptive of an application that drove
the initial post to this thread. A reply by Abby Spurdle seemed to me
to raise questions, whose answers may not be intelligible without
material snipp
Note my last response is probably off-topic.
I just wanted to highlight the need for defining problems in a way that
computers (and R programmers) can understand.
On Sun, Jul 14, 2019 at 1:13 PM Abby Spurdle wrote:
>
> Firstly, we don't really need all your working.
> Just the problem you want so
Firstly, we don't really need all your working.
Just the problem you want solve.
However, I'm still having difficulty understanding this.
> I'm observing Y[i] = (X[i]'b+e) given Y[i]>(z[i]'c+f) where e and
> f are normally distributed with standard deviations s and t,
> respectively, i = 1:n. I
> What integral?
What do you mean "What integral?"...
The integral on the Wikipedia page.
(The same page referenced in the earlier posts).
https://en.wikipedia.org/wiki/Truncated_distribution#Random_truncation
https://wikimedia.org/api/rest_v1/media/math/render/svg/93717ffcd3bfa2a60d825bd71b5375a
On 2019-07-12 22:31, Abby Spurdle wrote:
> The distribution of the randomly truncated variable has thus four
> parameters: a, b, mu and sigma. I was able to write down the likelihood
> and attempted to maximise it
I read the Wikipedia article more carefully.
The formula is relatively simple,
On 13/07/19 3:31 PM, Abby Spurdle wrote:
> The distribution of the randomly truncated variable has thus four
> parameters: a, b, mu and sigma. I was able to write down the likelihood
> and attempted to maximise it
I read the Wikipedia article more carefully.
The formula is relatively simp
> The distribution of the randomly truncated variable has thus four
> parameters: a, b, mu and sigma. I was able to write down the likelihood
> and attempted to maximise it
I read the Wikipedia article more carefully.
The formula is relatively simple, and is based on the application of Bayes
Theo
On 13/07/19 10:54 AM, Spencer Graves wrote:
Hello:
What do you suggest I do about modeling random truncation?
Good question! Probably the best answer is "Give up and go to the pub!" :-)
But seriously, there is a package DTDA on CRAN which purports to analyse
randomly truncated da
Did you search on e.g. "model truncation" at rseek.org? Several packages
came up that appear to deal with truncated data, though I have no clue
whether in the way you specify.
-- Bert
Bert Gunter
"The trouble with having an open mind is that people keep coming along and
sticking things into it."
> It would be nice if I had an R
> package that would make it relatively easy to model the truncation as a
> function of "d"
I suspect that R has everything you need, already.
However, I suspect you may need to reformulate your question to find what
you need.
> I assume that the probability of ob
Hello:
What do you suggest I do about modeling random truncation?
I have data on a variable Y in strata S[0], S[1], ..., S[n],
where Y is always observed in S[0] but is less often observed in the
other strata. I assume that the probability of observing Y is a
monotonically inc
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