[R] lm fails on some large input

[Fox, John]

Dear Dingyuan Wang,

But your question was answered clearly earlier in this thread (I forget by whom), showing that lm() provides the solution to the regression of x on y if the criterion for singularity is tightened:

> lm(x ~ y)

Call:
lm(formula = x ~ y)

Coefficients:
(Intercept)            y  
      94.73           NA  

> lm(x ~ y, tol=1e-10)

Call:
lm(formula = x ~ y, tol = 1e-10)

Coefficients:
(Intercept)            y  
 -2.403e+09    1.595e+00  

Best,
 John

> -----Original Message-----
> From: R-help [mailto:r-help-bounces using r-project.org] On Behalf Of Dingyuan
> Wang
> Sent: Thursday, April 18, 2019 12:36 PM
> To: Michael Dewey <lists using dewey.myzen.co.uk>; r-help using r-project.org
> Subject: Re: [R] lm fails on some large input
> 
> 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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> ______________________________________________
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