Extract R-square value with R in linear models [duplicate]

Question

I know that using summary will help me to do this manually, however, I will have to calculted tons of R-squared values. Therefore, I need the computer to extract it for me. Here is a simple example:

library(alr3) M.lm=lm(MaxSalary~Score,data=salarygov) #Here you will see the R square value summary(M.lm) 

How can I do that?

Answer 1

The R-squared, adjusted R-squared, and all other values you see in the summary are accessible from within the summary object. You can see everything by using str(summary(M.lm)):

> str(summary(M.lm))  # Truncated output... List of 11  $ call         : language lm(formula = MaxSalary ~ Score, data = salarygov)  $ terms        :Classes 'terms', 'formula' length 3 MaxSalary ~ Score  ...  $ residuals    : Named num [1:495] -232.3 -132.6 37.9 114.3 232.3 ...  $ coefficients : num [1:2, 1:4] 295.274 5.76 62.012 0.123 4.762 ...  $ aliased      : Named logi [1:2] FALSE FALSE  $ sigma        : num 507  $ df           : int [1:3] 2 493 2  $ r.squared    : num 0.817  $ adj.r.squared: num 0.816  $ fstatistic   : Named num [1:3] 2194 1 493  $ cov.unscaled : num [1:2, 1:2] 1.50e-02 -2.76e-05 -2.76e-05 5.88e-08 

To get the R-squared value, type summary(M.lm)$r.squared or summary(M.lm)$adj.r.squared

Answer 2

With one predictor you could simply use cor(salarygov$MaxSalary ,salarygov$Score)^2. Alternatively, summary(M.lm)$r.squared.