Estimates the penalty coefficient from the cross-validation criterion.
1 |
y |
The response vector. |
x |
A vector/matrix giving the values of the predictor
variable(s). If |
knots |
A vector givint the coordinates of the knots. |
degree |
The degree of the penalized smoothing spline. |
plot |
Logical. If |
n.points |
A numeric giving the number of CV computations needed to produce the plot. |
... |
Options to be passed to the |
For every linear smoother e.g. y.hat = S_λ y, the cross-validation criterion consists in minimizing the following quantity:
CV(λ) = ∑_{i=1}^n [(y_i - y.hat_i) / (1 - S_{λ,ii}) ]^2
where λ is the penalty coefficient, n the number of observations and S_{λ,ii} the i-th diagonal element of the matrix S_λ.
A list with components 'penalty', 'cv' and 'nlm.code' which give the
location of the minimum, the value of the cross-validation
criterion at that point and the code returned by the nlm
function - useful to assess for convergence issues.
Mathieu Ribatet
Ruppert, D. Wand, M.P. and Carrol, R.J. (2003) Semiparametric Regression Cambridge Series in Statistical and Probabilistic Mathematics.
cv
1 2 3 4 5 6 |
Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.
All documentation is copyright its authors; we didn't write any of that.