The final goal is to make two lines and find the intersection point.
I don't want to argue more about the reason.
The tol suggestion is reasonable, and I'll take that.
2019/4/19 4:12, 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 <gumb...@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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