[R] stats::lm has inconsistent output when adding constant to dependent variable

Mark Leeds
2019-09-27T18:43:28Z

correction to my previous answer. I looked around and I don't think it's
called the donsker effect. It seems to
jbe referred to as just  a case of "perfect separability.". if you
google
for" perfect separation in glms", you'll get a
lot of information.






On Fri, Sep 27, 2019 at 2:35 PM Mark Leeds <markleeds2 at gmail.com>
wrote:

> Hi: In your example, you made the response zero in every case which
> is going to cause problems.  In glm's, I think they call it the donsker
> effect. I'm not sure what it's called
> in OLS. probably a lack of identifiability. Note that you probably
> shouldn't be using zeros
> and 1's as the response in a regression anyway.
>
> If you change the response to below, you get what  you'd expect.
>
> y <- c(rep(0, 15), rep(1,15))
>
> On Fri, Sep 27, 2019 at 1:48 PM David J. Birke <djbirke at
berkeley.edu>
> wrote:
>
>> Dear R community,
>>
>> I just stumbled upon the following behavior in R version 3.6.0:
>>
>> set.seed(42)
>> y <- rep(0, 30)
>> x <- rbinom(30, 1, prob = 0.91)
>> # The following will not show any t-statistic or p-value
>> summary(lm(y~x))
>> #  The following will show t-statistic and p-value
>> summary(lm(1+y~x))
>>
>> My expected output is that the first case should report t-statistic and
>> p-value. My intuition might be tricking me, but I think that a constant
>> shift of the data should be fully absorbed by the constant and not
>> affect inference about the slope.
>>
>> Is this a bug or is there a reason why there should be a discrepancy
>> between the two outputs?
>>
>> Best,
>> David
>>
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