CodeGuy CodeGuy - 8 months ago 42
R Question

Determining the goodness of an R fit using lm()

I learned to get a linear fit with some points using lm in my R script. So, I did that (which worked nice), and printed out the fit:

lm(formula = y2 ~ x2)

1 2 3 4
5.000e+00 -1.000e+01 5.000e+00 7.327e-15

Estimate Std. Error t value Pr(>|t|)
(Intercept) 70.000 17.958 3.898 0.05996 .
x2 85.000 3.873 21.947 0.00207 **
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 8.66 on 2 degrees of freedom
Multiple R-squared: 0.9959, Adjusted R-squared: 0.9938
F-statistic: 481.7 on 1 and 2 DF, p-value: 0.00207

I'm trying to determine the best way to judge how great this fit is. I need to compare this fit with a few others (which are also linear using
function). What value from this summary would be the best way to judge how good this fit is? I was thinking to use the residual standard error. Any suggestions. Also, how do I extract that value from the fit variable?


If you want to access the pieces produced by summary directly, you can just call summary and store the result in a variable and then inspect the resulting object:

rs <- summary(lm1)

Perhaps rs$sigma is what you're looking for?


Before someone chides me, I should point out that for some of this information, this is not the recommended way to access it. Rather you should use the designated extractors like residuals() or coef.