[R] lm fails on some large input

[Jeff Newmiller]

The fact that you think x~y is interchangeable with y~x suggests to me that you will have a difficult time convincing R Core that this is a bug. I recommend that you take at leastan upper division college course in linear regression first.

On April 18, 2019 9:35:55 AM PDT, Dingyuan Wang <gumblex using aosc.io> wrote:
>I just want to make a line out of timestamps vs some coordinates, so
>y~x 
>or x~y doesn't matter.
>
>Yes, I know the answer. When trying R, I'm surprised that R can't solve
>
>that either. I first noticed that PostgreSQL can't solve it, and found 
>that they fixed that in pg 12.
>
>https://www.postgresql.org/message-id/153313051300.1397.9594490737341194671%40wrigleys.postgresql.org
>
>Therefore I come to ask whether someone know how to fix this in R, or I
>
>must submit it as a bug?
>
>2019/4/18 23:24, Michael Dewey:
>> Perhaps subtract 1506705766 from y?
>> 
>> Saying some other software does it well implies you know what the 
>> _correct_ answer is here but I would question what that means with
>this 
>> sort of data-set.
>> 
>> On 17/04/2019 07:26, Dingyuan Wang wrote:
>>> Hi,
>>>
>>> This input doesn't have any interesting properties except y is unix 
>>> time. Spreadsheets can do this well.
>>> Is this a bug that lm can't do x ~ y?
>>>
>>> R version 3.5.2 (2018-12-20) -- "Eggshell Igloo"
>>> Copyright (C) 2018 The R Foundation for Statistical Computing
>>> Platform: x86_64-pc-linux-gnu (64-bit)
>>>
>>>  > x = c(79.744, 123.904, 87.29601, 116.352, 67.71201, 72.96001, 
>>> 101.632, 108.928, 94.08)
>>>  > y = c(1506705739.385, 1506705766.895, 1506705746.293, 
>>> 1506705761.873, 1506705734.743, 1506705735.351, 1506705756.26, 
>>> 1506705761.307, 1506705747.372)
>>>  > m = lm(x ~ y)
>>>  > summary(m)
>>>
>>> Call:
>>> lm(formula = x ~ y)
>>>
>>> Residuals:
>>>       Min       1Q   Median       3Q      Max
>>> -27.0222 -14.9902  -0.6542  14.1938  29.1698
>>>
>>> Coefficients: (1 not defined because of singularities)
>>>              Estimate Std. Error t value Pr(>|t|)
>>> (Intercept)   94.734      6.511   14.55 4.88e-07 ***
>>> y                 NA         NA      NA       NA
>>> ---
>>> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
>>>
>>> Residual standard error: 19.53 on 8 degrees of freedom
>>>
>>>  > summary(lm(y ~ x))
>>>
>>> Call:
>>> lm(formula = y ~ x)
>>>
>>> Residuals:
>>>      Min      1Q  Median      3Q     Max
>>> -2.1687 -1.3345 -0.9466  1.3826  2.6551
>>>
>>> Coefficients:
>>>               Estimate Std. Error   t value Pr(>|t|)
>>> (Intercept) 1.507e+09  3.294e+00 4.574e+08  < 2e-16 ***
>>> x           6.136e-01  3.413e-02 1.798e+01 4.07e-07 ***
>>> ---
>>> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
>>>
>>> Residual standard error: 1.885 on 7 degrees of freedom
>>> Multiple R-squared:  0.9788,    Adjusted R-squared:  0.9758
>>> F-statistic: 323.3 on 1 and 7 DF,  p-value: 4.068e-07
>>>
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>>
>
>______________________________________________
>R-help using r-project.org mailing list -- To UNSUBSCRIBE and more, see
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-- 
Sent from my phone. Please excuse my brevity.