Same version on Mac, same results.
> On 6 Sep 2017, at 15:22, JRG wrote:
>
> Indeed (version-specific).
>
> With R 3.4.1 on linux, I get coefficients and residuals that are
> numerically exact, F-statistic = NaN, p-value = NA, R-squared = NaN, etc.
>
> All of which is what ought to happen, gi
Indeed (version-specific).
With R 3.4.1 on linux, I get coefficients and residuals that are
numerically exact, F-statistic = NaN, p-value = NA, R-squared = NaN, etc.
All of which is what ought to happen, given that the response variable
(y) is not actually variable.
---JRG
John R. Gleason
On
> I think what you're seeing is
> https://en.wikipedia.org/wiki/Loss_of_significance.
Almost.
All the results in the OP's summary are reflections of finite precision in the
analytically exact solution, leading to residuals smaller than the double
precision limit. The summary is correctly warnin
Tim,
I think what you're seeing is
https://en.wikipedia.org/wiki/Loss_of_significance.
Cheers,
Mark
From: "Glover, Tim"
To: "r-help@r-project.org"
Date: 09/05/2017 11:37 AM
Subject:[R] Interesting behavior of lm() with small, problematic
data sets
Sent by:"R-help"
> On Sep 5, 2017, at 6:24 AM, Glover, Tim wrote:
>
> I've recently come across the following results reported from the lm()
> function when applied to a particular type of admittedly difficult data.
> When working with
> small data sets (for instance 3 points) with the same response for diffe
Why does an unreliable fit have to provide "reasonable" results?
More specifically, p-values arise from observed distributions... if your slopes
are "in the noise" then the slope estimate's location within that distribution
could be anywhere relative to the center and spread of that very narrow
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