# Diagnostics in smoothing splines

I'm studying about Smoothing Splines and I'm having some doubts about this method. I already understood the criterion to choose the smooth parameter, but How I acess the fit of this type of non-parametric regression? Residuals? Just looking the fit in the plot?

All that I get as output from smooth.spline() in R is below

Smoothing Parameter  spar= 0.9  lambda= 0.007442127
Equivalent Degrees of Freedom (Df): 5.384943
Penalized Criterion: 4357.647
GCV: 32.18273


One more doubt, the smooth parameter is spar or lambda? I know that there exists some relationship between the two values.

age <- c(0.083, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 10.5, 11.0, 11.5, 12.0, 12.5, 13.0, 13.5, 14.0, 14.5, 15.0, 15.5, 16.0, 16.5, 17.0, 18.0, 19.0, 20.0)
height<- c(525.0, 608.0, 665.0, 717.0, 745.0, 803.0, 859.0, 940, 1007, 1065, 1121, 1183, 1238, 1298, 1348, 1369, 1391, 1422, 1470, 1525, 1578, 1638, 1664, 1692, 1708, 1723, 1727, 1727, 1727, 1729, 1738, 1738)
plot(age,height,main="Age vs Height",xlab="Age",ylab="Height")
lines(smooth.spline(age,height),col='red')


What data is used to fit this curve?