I would like to calculate the R-Squared and p-value (F-Statistics) for my model (with Standard Robust Errors). Can someone explain to me how to get them for the adapted model (modrob)?

The regression without standard robust error:

>mod=lm(giniA~region+dummy_2009+age,data=fors)
Call:
lm(formula = giniA ~ region + dummy_2009 + age, data = fors)
Residuals:
Min 1Q Median 3Q Max
-0.13714 -0.01402 0.00192 0.01458 0.09922
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 6.167e-01 1.917e-03 321.749 < 2e-16 ***
regionWest 2.218e-03 1.408e-03 1.575 0.115
dummy_2009 -2.225e-02 1.205e-03 -18.473 < 2e-16 ***
age -1.858e-04 3.858e-05 -4.816 1.6e-06 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.02381 on 1624 degrees of freedom
Multiple R-squared: 0.2034, Adjusted R-squared: 0.202
F-statistic: 138.3 on 3 and 1624 DF, p-value: < 2.2e-16

And this is my regression with standard robust errors, for which I would like to calculate the R-squared and p-value(F-statistics):

# model with robust standard errors:
> modrob = coeftest(mod,vcov. = vcovHAC)
t test of coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 6.1666e-01 2.0404e-03 302.2289 < 2.2e-16 ***
regionWest 2.2179e-03 1.4474e-03 1.5324 0.1256
dummy_2009 -2.2254e-02 1.1846e-03 -18.7869 < 2.2e-16 ***
age -1.8582e-04 4.2784e-05 -4.3431 1.491e-05 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

I am not a statistician, but I believe the R squared is not changed by the fact you use robust regression. After looking it up, they say indeed it's the same and it's not shown because you don't trust this statistic when you do the robust regression (that's the point you're doing it).

See this post:

FYI, if you like to extract the R squared from the original model, you can: