[R] Increasing number of observations worsen the regression model
Raffa
r@||@m@|den @end|ng |rom gm@||@com
Sun May 26 16:09:12 CEST 2019
I have solved the problem. It was caused by Intel MKL. Uninstalling
Intel MKL and using OpenBLAS instead fixed the problem completely.
Notice that, using Intel MKL, I was able to reproduce the problem by
computing the regression coefficients directly from the usual formula,
which were returned completely wrong.
My guess is that some optimization of Intel MKL which is activated in
"large" matrices give completely wrong result (I don't know which
operation exactly)
Thanks,
Best,
Raffaele Mancuso
On 26/05/19 16:06, Fox, John wrote:
> Dear Raffaele,
>
> Using your code, with one modification -- setting the seed for R's random number generator to make the result reproducible -- I get:
>
>> set.seed(12345)
> . . .
>
>> lmMod <- lm(yvar~xvar)
>> print(summary(lmMod))
> Call:
> lm(formula = yvar ~ xvar)
>
> Residuals:
> Min 1Q Median 3Q Max
> -4.0293 -0.6732 0.0021 0.6749 4.2883
>
> Coefficients:
> Estimate Std. Error t value Pr(>|t|)
> (Intercept) 1.0057713 0.0057529 174.8 <2e-16 ***
> xvar 2.0000889 0.0009998 2000.4 <2e-16 ***
> ---
> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
>
> Residual standard error: 0.9964 on 29998 degrees of freedom
> Multiple R-squared: 0.9926, Adjusted R-squared: 0.9926
> F-statistic: 4.002e+06 on 1 and 29998 DF, p-value: < 2.2e-16
>
> which is more or less what one would expect.
>
> My guess: you've saved your R workspace from a previous session, and it is then loaded at the start of your R session; something in the saved workspace is affecting the result, although frankly I can't think what that might be.
>
> I hope this helps,
> John
>
> -----------------------------------------------------------------
> John Fox
> Professor Emeritus
> McMaster University
> Hamilton, Ontario, Canada
> Web: https://socialsciences.mcmaster.ca/jfox/
>
>
>
>> -----Original Message-----
>> From: R-help [mailto:r-help-bounces using r-project.org] On Behalf Of Raffa
>> Sent: Saturday, May 25, 2019 8:38 AM
>> To: r-help using r-project.org
>> Subject: [R] Increasing number of observations worsen the regression model
>>
>> I have the following code:
>>
>> ```
>>
>> rm(list=ls())
>> N = 30000
>> xvar <- runif(N, -10, 10)
>> e <- rnorm(N, mean=0, sd=1)
>> yvar <- 1 + 2*xvar + e
>> plot(xvar,yvar)
>> lmMod <- lm(yvar~xvar)
>> print(summary(lmMod))
>> domain <- seq(min(xvar), max(xvar)) # define a vector of x values to feed
>> into model lines(domain, predict(lmMod, newdata =
>> data.frame(xvar=domain))) # add regression line, using `predict` to generate
>> y-values
>>
>> ```
>>
>> I expected the coefficients to be something similar to [1,2]. Instead R keeps
>> throwing at me random numbers that are not statistically significant and don't
>> fit the model, and I have 20k observations. For example
>>
>> ```
>>
>> Call:
>> lm(formula = yvar ~ xvar)
>>
>> Residuals:
>> Min 1Q Median 3Q Max
>> -21.384 -8.908 1.016 10.972 23.663
>>
>> Coefficients:
>> Estimate Std. Error t value Pr(>|t|)
>> (Intercept) 0.0007145 0.0670316 0.011 0.991
>> xvar 0.0168271 0.0116420 1.445 0.148
>>
>> Residual standard error: 11.61 on 29998 degrees of freedom Multiple R-
>> squared: 7.038e-05, Adjusted R-squared: 3.705e-05
>> F-statistic: 2.112 on 1 and 29998 DF, p-value: 0.1462
>>
>> ```
>>
>>
>> The strange thing is that the code works perfectly for N=200 or N=2000.
>> It's only for larger N that this thing happen U(for example, N=20000). I have
>> tried to ask for example in CrossValidated
>> <https://stats.stackexchange.com/questions/410050/increasing-number-of-
>> observations-worsen-the-regression-model>
>> but the code works for them. Any help?
>>
>> I am runnign R 3.6.0 on Kubuntu 19.04
>>
>> Best regards
>>
>> Raffaele
>>
>>
>> [[alternative HTML version deleted]]
>>
>> ______________________________________________
>> R-help using 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.
More information about the R-help
mailing list