Obtaining r-squared values from cumSeg/step-function fit

Question

I know its possible to extract r-squared values to quantify the 'goodness-of-fit' of regressions in R, with something to the effect of:

fit <- lm(y ~ x1 + x2 + x3, data=mydata)  # Not actual data
r-sq <- summary(fit)$r.squared # or $adj.r.squared

I've recently been using the cumSeg package for step-function regressions, but it doesn't appear to offer this functionality, though it does provide residuals as a vector.

Is there some way to extract an r-squared (or adj. r squared) that I don't know about? Or can it be calculated 'de novo' with something that cumSeg does actually provide?

EDIT This is the output of summary() for my stepfunction created via cumSeg. Perhaps someone more mathematically versed with stepfunctions knows if the nomenclature for an r-squared (or whatever the equivalent is) is just different and the data I'm looking for is actually there (or if it is even a legitimate question to ask for an R-squared for stepfunctions?! I'm assuming it should be calculable from any fitted model really.

> summary(stepfunc)
              Length Class  Mode   
coefficients   3     -none- numeric
residuals     16     -none- numeric
effects       16     -none- numeric
rank           1     -none- numeric
fitted.values 16     -none- numeric
assign         0     -none- NULL   
qr             5     qr     list   
df.residual    1     -none- numeric
epsilon        1     -none- numeric
it             1     -none- numeric
psi            1     -none- numeric
beta.c         1     -none- numeric
gamma.c        1     -none- numeric 
V             16     -none- numeric
y             16     -none- numeric
id.group      16     -none- numeric
est.means      2     -none- numeric
n.psi          1     -none- numeric

Answer 1

You can extract what you need directly from your result with squared brackets []. Here an example:

set.seed(your seed)    

dt <- data.frame(fc = rep(sample(0:100, 100, replace = TRUE), 
100), v1 =     rep(rnorm(100, 0, 1), 100), v2 = rep(rnorm(100, 0, 2), 100))

fit <- lm(fc ~ v1 + v2, data = dt)  # Not actual data

summary(fit)[9]

$adj.r.squared
[1] 0.01484017

Otherwise, you can look for the $ operator in this way:

summary(fit$effects)

It will show you a list of possible parameters you could be interested in.