Re: [R] lm fails on some large input
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 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 __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. --- This email has been checked for viruses by AVG. https://www.avg.com __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] lm fails on some large input
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+091.595e+00 Best, John > -Original Message- > From: R-help [mailto:r-help-boun...@r-project.org] On Behalf Of Dingyuan > Wang > Sent: Thursday, April 18, 2019 12:36 PM > To: Michael Dewey ; r-help@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 > >> > >> __ > >> R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see > >> https://stat.ethz.ch/mailman/listinfo/r-help > >> PLEASE do read the posting guide > >> http://www.R-project.org/posting-guide.html > >> and provide commented, minimal, self-contained, reproducible code. > >> > >> --- > >> This email has been checked for viruses by AVG. > >> https://www.avg.com > >> > >> > > > > __ > R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting- > guide.html > and provide commented, minimal, self-contained, reproducible code. __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] lm fails on some large input
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 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 >>> >>> __ >>> R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see >>> https://stat.ethz.ch/mailman/listinfo/r-help >>> PLEASE do read the posting guide >>> http://www.R-project.org/posting-guide.html >>> and provide commented, minimal, self-contained, reproducible code. >>> >>> --- >>> This email has been checked for viruses by AVG. >>> https://www.avg.com >>> >>> >> > >__ >R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see >https://stat.ethz.ch/mailman/listinfo/r-help >PLEASE do read the posting guide >http://www.R-project.org/posting-guide.html >and provide commented, minimal, self-contained, reproducible code. -- Sent from my phone. Please excuse my brevity. __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
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 __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. --- This email has been checked for viruses by AVG. https://www.avg.com __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] lm fails on some large input
I make a general rule not to stick time values into numerical analysis
algorithms without first subtracting a reasonable epoch (to obtain difftime)
and then using as.numeric.POSIXt with the units argument set explicitly so the
analysis uses numeric values that I can interpret. While the explicit use of
difftime function does something similar, if any other operations are performed
on it the units could change again before the inevitable conversion to numeric
occurs somewhere down the line so I think taking responsibility for the numeric
conversion myself is less likely to leave surprises.
On April 18, 2019 9:32:09 AM PDT, William Dunlap via R-help
wrote:
>This sort of data arises quite easily if you deal with time/dates
>around
>now. E.g.,
>
>> d <- data.frame(
>+ when = seq(as.POSIXct("2017-09-29 18:22:01"), by="secs", len=10),
>+ measurement = log2(1:10))
>> coef(lm(data=d, measurement ~ when))
> (Intercept) when
>2.1791061114716954 NA
>> as.numeric(d$when)[1:2]
>[1] 1506734521 1506734522
>
>There are problems with the time units (seconds vs. hours) if you
>subtract
>off a time because the units of -.POSIXt depend on the data:
>
>> coef(lm(data=d, measurement ~ I(when - min(when
>(Intercept) I(when - min(when))
>0.68327571513124297 0.33240675474232279
>> coef(lm(data=d, measurement ~ I(when - as.POSIXct("2017-09-29
>00:00:00"
>(Intercept) I(when - as.POSIXct("2017-09-29
>00:00:00"))
> -21978.3837546251634
>1196.6643170736229
>
>
>Hence you have to use difftime and specify the units
>
>> coef(lm(data=d, measurement ~ difftime(when, as.POSIXct("2017-09-29
>00:00:00"), units="secs")))
> (Intercept)
> -2.1978383754612696e+04
>difftime(when, as.POSIXct("2017-09-29 00:00:00"), units = "secs")
> 3.3240675474248449e-01
>> coef(lm(data=d, measurement ~ difftime(when, min(when),
>units="secs")))
> (Intercept) difftime(when, min(when), units =
>"secs")
> 0.68327571513124297
> 0.33240675474232279
>
>
>
>Bill Dunlap
>TIBCO Software
>wdunlap tibco.com
>
>
>On Thu, Apr 18, 2019 at 8:24 AM Michael Dewey
>wrote:
>
>> 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
>> >
>> > __
>> > R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
>> > https://stat.ethz.ch/mailman/listinfo/r-help
>> > PLEASE do read the posting guide
>> > http://www.R-project.org/posting-guide.html
>> > and provide commented, minimal, self-contained, reproducible code.
>> >
>> > ---
>> > This email has been checked for viruses by AVG.
>> > https://www.avg.com
>> >
>> >
>>
>> --
>> Michael
>> http://www.dewey.myzen.co.uk/home.html
>>
>> __
Re: [R] lm fails on some large input
Dear Peter, > -Original Message- > From: peter dalgaard [mailto:pda...@gmail.com] > Sent: Thursday, April 18, 2019 12:23 PM > To: Fox, John > Cc: Michael Dewey ; Dingyuan Wang > ; r-help@r-project.org > Subject: Re: [R] lm fails on some large input > > Um, you need to reverse y and x there. The question was about lm(y ~ x) > Good catch! I missed that in the original posting, and lm() does indeed produce the LS solution for the regression of y on x. And, as I'd have expected, the naïve approach also fails for the regression of x on y: > Y <- cbind(1, y) > b <- solve(t(Y) %*% Y) %*% t(Y) %*% x Error in solve.default(t(Y) %*% Y) : system is computationally singular: reciprocal condition number = 6.19587e-35 resolving the mystery. Thanks, John > > X <- cbind(1, y) > > solve(crossprod(X)) > Error in solve.default(crossprod(X)) : > system is computationally singular: reciprocal condition number = 6.19587e- > 35 > > Actually, lm can QR perfectly OK, but it gets caught by its singularity > detection: > > > qr <- qr(X, tol=1e-10) > > qr # without the tol bit, you get same thing but $rank == 1 > $qr > y > [1,] -3.000 -4.520117e+09 > [2,] 0.333 -3.426530e+01 > [3,] 0.333 -2.947103e-02 > [4,] 0.333 4.252164e-01 > [5,] 0.333 -3.665468e-01 > [6,] 0.333 -3.488029e-01 > [7,] 0.333 2.614064e-01 > [8,] 0.333 4.086982e-01 > [9,] 0.333 2.018556e-03 > > $rank > [1] 2 > > $qraux > [1] 1.33 1.571779 > > $pivot > [1] 1 2 > > attr(,"class") > [1] "qr" > > x = c(79.744, 123.904, 87.29601, 116.352, 67.71201, 72.96001, 101.632, > > 108.928, 94.08) > > qr.coef(qr,x) > y > -2.403345e+09 1.595099e+00 > > > 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+091.595e+00 > > > lm(x~I(y-mean(y))) > > Call: > lm(formula = x ~ I(y - mean(y))) > > Coefficients: >(Intercept) I(y - mean(y)) > 94.734 1.595 > > > > On 18 Apr 2019, at 17:56 , Fox, John wrote: > > > > Dear Michael and Dingyuan Wang, > > > >> -Original Message- > >> From: R-help [mailto:r-help-boun...@r-project.org] On Behalf Of > >> Michael Dewey > >> Sent: Thursday, April 18, 2019 11:25 AM > >> To: Dingyuan Wang ; r-help@r-project.org > >> Subject: Re: [R] lm fails on some large input > >> > >> 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. > > > > It's rather an interesting problem, though, because the naïve computation of > the LS solution works: > > > > plot(x, y) > > X <- cbind(1, x) > > b <- solve(t(X) %*% X) %*% t(X) %*% y > > b > > abline(b) > > > > That surprised me, because I expected that lm() computation, using the QR > decomposition, would be more numerically stable. > > > > Best, > > John > > > > - > > John Fox > > Professor Emeritus > > McMaster University > > Hamilton, Ontario, Canada > > Web: https://socialsciences.mcmaster.ca/jfox/ > > > > > > > >> > >> 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) > >&g
Re: [R] lm fails on some large input
> On Apr 18, 2019, at 8:24 AM, Michael Dewey wrote: > > Perhaps subtract 1506705766 from y? Good advice. Some further notes follow. One can specify `tol` to have a smaller than default value e.g. m2 <- lm(x ~ y, tol=1e-12) which is accurate: plot(y,x) abline(coef=coef(m2)) Users of numerical procedures need to be mindful of the default settings of the algorithms they use. As is well known, the use of a too large default for convergence of an optimization algorithm can lead to seriously wrong results. There is an example described here: https://science.sciencemag.org/content/296/5575/1945/tab-pdf One might quibble with the choice of tol=1e-7 (the default in lm.fit), and 64 bit floating point will support much smaller values. However, there are usually statistical issues surrounding fitting highly collinear variables. So, `tol = 1e-07` seems more like a feature than a bug. HTH, Chuck > > 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 >> __ >> R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see >> https://stat.ethz.ch/mailman/listinfo/r-help >> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html >> and provide commented, minimal, self-contained, reproducible code. >> --- >> This email has been checked for viruses by AVG. >> https://www.avg.com > > -- > Michael > http://www.dewey.myzen.co.uk/home.html > __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] lm fails on some large input
This sort of data arises quite easily if you deal with time/dates around
now. E.g.,
> d <- data.frame(
+ when = seq(as.POSIXct("2017-09-29 18:22:01"), by="secs", len=10),
+ measurement = log2(1:10))
> coef(lm(data=d, measurement ~ when))
(Intercept) when
2.1791061114716954 NA
> as.numeric(d$when)[1:2]
[1] 1506734521 1506734522
There are problems with the time units (seconds vs. hours) if you subtract
off a time because the units of -.POSIXt depend on the data:
> coef(lm(data=d, measurement ~ I(when - min(when
(Intercept) I(when - min(when))
0.68327571513124297 0.33240675474232279
> coef(lm(data=d, measurement ~ I(when - as.POSIXct("2017-09-29
00:00:00"
(Intercept) I(when - as.POSIXct("2017-09-29
00:00:00"))
-21978.3837546251634
1196.6643170736229
Hence you have to use difftime and specify the units
> coef(lm(data=d, measurement ~ difftime(when, as.POSIXct("2017-09-29
00:00:00"), units="secs")))
(Intercept)
-2.1978383754612696e+04
difftime(when, as.POSIXct("2017-09-29 00:00:00"), units = "secs")
3.3240675474248449e-01
> coef(lm(data=d, measurement ~ difftime(when, min(when), units="secs")))
(Intercept) difftime(when, min(when), units =
"secs")
0.68327571513124297
0.33240675474232279
Bill Dunlap
TIBCO Software
wdunlap tibco.com
On Thu, Apr 18, 2019 at 8:24 AM Michael Dewey
wrote:
> 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
> >
> > __
> > R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
> > https://stat.ethz.ch/mailman/listinfo/r-help
> > PLEASE do read the posting guide
> > http://www.R-project.org/posting-guide.html
> > and provide commented, minimal, self-contained, reproducible code.
> >
> > ---
> > This email has been checked for viruses by AVG.
> > https://www.avg.com
> >
> >
>
> --
> Michael
> http://www.dewey.myzen.co.uk/home.html
>
> __
> R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide
> http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>
[[alternative HTML version deleted]]
__
R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
Re: [R] lm fails on some large input
Um, you need to reverse y and x there. The question was about lm(y ~ x) > X <- cbind(1, y) > solve(crossprod(X)) Error in solve.default(crossprod(X)) : system is computationally singular: reciprocal condition number = 6.19587e-35 Actually, lm can QR perfectly OK, but it gets caught by its singularity detection: > qr <- qr(X, tol=1e-10) > qr # without the tol bit, you get same thing but $rank == 1 $qr y [1,] -3.000 -4.520117e+09 [2,] 0.333 -3.426530e+01 [3,] 0.333 -2.947103e-02 [4,] 0.333 4.252164e-01 [5,] 0.333 -3.665468e-01 [6,] 0.333 -3.488029e-01 [7,] 0.333 2.614064e-01 [8,] 0.333 4.086982e-01 [9,] 0.333 2.018556e-03 $rank [1] 2 $qraux [1] 1.33 1.571779 $pivot [1] 1 2 attr(,"class") [1] "qr" > x = c(79.744, 123.904, 87.29601, 116.352, 67.71201, 72.96001, 101.632, > 108.928, 94.08) > qr.coef(qr,x) y -2.403345e+09 1.595099e+00 > 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+091.595e+00 > lm(x~I(y-mean(y))) Call: lm(formula = x ~ I(y - mean(y))) Coefficients: (Intercept) I(y - mean(y)) 94.734 1.595 > On 18 Apr 2019, at 17:56 , Fox, John wrote: > > Dear Michael and Dingyuan Wang, > >> -Original Message- >> From: R-help [mailto:r-help-boun...@r-project.org] On Behalf Of Michael >> Dewey >> Sent: Thursday, April 18, 2019 11:25 AM >> To: Dingyuan Wang ; r-help@r-project.org >> Subject: Re: [R] lm fails on some large input >> >> 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. > > It's rather an interesting problem, though, because the naïve computation of > the LS solution works: > > plot(x, y) > X <- cbind(1, x) > b <- solve(t(X) %*% X) %*% t(X) %*% y > b > abline(b) > > That surprised me, because I expected that lm() computation, using the QR > decomposition, would be more numerically stable. > > Best, > John > > - > John Fox > Professor Emeritus > McMaster University > Hamilton, Ontario, Canada > Web: https://socialsciences.mcmaster.ca/jfox/ > > > >> >> 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
Re: [R] lm fails on some large input
Dear Michael and Dingyuan Wang, > -Original Message- > From: R-help [mailto:r-help-boun...@r-project.org] On Behalf Of Michael > Dewey > Sent: Thursday, April 18, 2019 11:25 AM > To: Dingyuan Wang ; r-help@r-project.org > Subject: Re: [R] lm fails on some large input > > 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. It's rather an interesting problem, though, because the naïve computation of the LS solution works: plot(x, y) X <- cbind(1, x) b <- solve(t(X) %*% X) %*% t(X) %*% y b abline(b) That surprised me, because I expected that lm() computation, using the QR decomposition, would be more numerically stable. Best, John - John Fox Professor Emeritus McMaster University Hamilton, Ontario, Canada Web: https://socialsciences.mcmaster.ca/jfox/ > > 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 > > > > __ > > R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see > > https://stat.ethz.ch/mailman/listinfo/r-help > > PLEASE do read the posting guide > > http://www.R-project.org/posting-guide.html > > and provide commented, minimal, self-contained, reproducible code. > > > > --- > > This email has been checked for viruses by AVG. > > https://www.avg.com > > > > > > -- > Michael > http://www.dewey.myzen.co.uk/home.html > > __ > R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting- > guide.html > and provide commented, minimal, self-contained, reproducible code. __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] lm fails on some large input
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 __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. --- This email has been checked for viruses by AVG. https://www.avg.com -- Michael http://www.dewey.myzen.co.uk/home.html __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
[R] lm fails on some large input
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 __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
