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Get error value from linear regression function lm

I have a linear regression problem that I solved with:

m=lm(value ~ mean, data=d)

and from this value i you can get R2 and the regression equation.

but I want to get a standard error (installation error). i was able to see the value, but i don't know how to get it to store it in the dataframe.

I get the value using summary(m)

and the result looks something like this:

Call:
lm(formula = value ~ mean, data = d)

Residuals:
    Min      1Q  Median      3Q     Max 
-25.000 -15.909  -2.124  14.596  44.697 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)  2.500e+01  1.064e+00   23.49   <2e-16 ***
mean        -1.759e-06  1.536e+00    0.00        1    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 

Residual standard error: 16.85 on 1298 degrees of freedom
Multiple R-squared: 1.01e-15,   Adjusted R-squared: -0.0007704 
F-statistic: 1.311e-12 on 1 and 1298 DF,  p-value: 1

so the question is, how can I access these values?

Thank you

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3 answers


The function summary

simply returns a list R.

##Generate some dummy data
x = runif(10);y = runif(10)
m = summary(lm(y ~ x))

We can use the normal list syntax to retrieve what we want. For example,

m[[4]]

Returns the model data frame fits



R> m[[4]]
            Estimate Std. Error t value Pr(>|t|)
(Intercept)  0.44265     0.2443  1.8123   0.1075
x            0.07066     0.4460  0.1584   0.8781

and m[[6]]

returnsResidual standard error 

R> m[[6]]
[1] 0.2928

There are several handy functions such as coefficients(m)

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Access to balances using resid(m)

.



EDIT: From the comments, it sounds like what you want sum(resid(m) ^ 2)

.

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Extract residual standard deviation "Sigma"

sigma(m)
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