> Yes, and I think that the suggestion in another post to look at censored
> regression is more in the right direction.
I think this is right and perhaps the best (or at least better) pathway to
pursue than considering this within the framework of measurement error (ME). Of
course there *is* M
: Ravi Varadhan; r-help@r-project.org
Subject: Re: [R] Linear regression with a rounded response variable
> On 21 Oct 2015, at 19:57 , Charles C. Berry wrote:
>
> On Wed, 21 Oct 2015, Ravi Varadhan wrote:
>
>> [snippage]
>
> If half the subjects have a value of 5 second
Hi Ravi,
And remember that the vanilla rounding procedure is biased upward. That is,
an observation of 5 actually may have ranged from 4.5 to 5.4.
Jim
On Thu, Oct 22, 2015 at 7:15 AM, peter salzman
wrote:
> here is one thought:
>
> if you plug in your numbers into any kind of regression you wil
> On 21 Oct 2015, at 19:57 , Charles C. Berry wrote:
>
> On Wed, 21 Oct 2015, Ravi Varadhan wrote:
>
>> [snippage]
>
> If half the subjects have a value of 5 seconds and the rest are split between
> 4 and 6, your assertion that rounding induces an error of
> dunif(epsilon,-0.5,0.5) is surely
here is one thought:
if you plug in your numbers into any kind of regression you will get
prediction that are real numbers and not necessarily integers, it may be
that you predictions are good enough with this approximate value of Y. you
could test this by randomly shuffling your data by +- 0.5 an
This could be modeled directly using Bayesian techniques. Consider the
Bayesian version of the following model where we only observe y and X. y0
is not observed.
y0 <- X b + error
y <- round(y0)
The following code is based on modifying the code in the README of the CRAN
rcppbugs R package.
Hi Ravi,
Thanks for this interesting question. My thoughts are given below.
If you believe the rounding is indeed uniformly distributed, then the
problem is equivalent with adding a uniform random error between (-0.5,
0.5) for every observation in addition to the standard normal error, which
will
On Wed, 21 Oct 2015, Ravi Varadhan wrote:
Hi, I am dealing with a regression problem where the response variable,
time (second) to walk 15 ft, is rounded to the nearest integer. I do
not care for the regression coefficients per se, but my main interest is
in getting the prediction equation fo
8 matches
Mail list logo
