Re: [R] Question about linear models

From: David Winsemius <dwinsemius_at_comcast.net>
Date: Tue, 18 Nov 2008 23:55:30 -0500

You can always inflate the SS by using smaller units, which is what your log transformation is doing. What is important for inference is the ratios of those sums of squares. The rest of your homework is something you will need to complete yourself.

http://www.ugr.es/~falvarez/relaMetodos2.pdf ..... see Question 9 http://www.uclm.es/profesorado/jesuslopezfidalgo/MODELOS.pdf ...... see Question 47

-- 
David Winsemius, MD
Heritage Labs


On Nov 18, 2008, at 11:44 PM, Ricardo Ríos wrote:


> Hi wizards,
>
> I have the following model:
>
> x<-c(20.79, 22.40, 23.15, 23.89, 24.02, 25.14, 28.49, 29.04, 29.88,
> 30.06)
> y <- c(194.5, 197.9, 199.4, 200.9, 201.4, 203.6, 209.5, 210.7,
> 211.9, 212.2)
> model1 <- lm( y ~ x )
> anova(model1)
>
> Df Sum Sq Mean Sq F value Pr(>F)
> x 1 368.87 368.87 4384.6 3.011e-12 ***
> Residuals 8 0.67 0.08
>
>
> But, I have realized the following transformation:
>
> lnx <- log(x)
> lny <- log(y)
> model2 <- lm( lny ~ lnx )
> anova(model2)
>
> Response: lny
> Df Sum Sq Mean Sq F value Pr(>F)
> lnx 1 0.0088620 0.0088620 27234 2.034e-15 ***
> Residuals 8 0.0000026 0.0000003
>
>
>
> The second model has a Sum of square Residuals very small
>
> I have analyzed the following graph:
>
> plot( model1$fitted.values, model1$residuals)
> plot( model2$fitted.values, model2$residuals)
>
>
> I have observed that maybe the first model has a specification error.
> is that correct? Which model is the best?
>
> I was trying to get information about it, but I did not found
> anything.
>
>
> Thanks in advance
>
> --
> http://ricardorios.wordpress.com/
>
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______________________________________________ R-help_at_r-project.org mailing list 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.
Received on Wed 19 Nov 2008 - 04:57:20 GMT

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