Description Usage Arguments Details Value Author(s) References See Also Examples
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.
1 2 3 4 5 6 |
$cv
[1] 91.60301
$penalty
[1] 0.2106398
$nlm.code
[1] 1
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