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