cv {SpatialExtremes} | R Documentation |
Estimates the penalty coefficient from the cross-validation criterion.
cv(y, x, knots, degree, plot = TRUE, n.points = 150, ...)
y |
The response vector. |
x |
A vector/matrix giving the values of the predictor
variable(s). If x is a matrix, each row corresponds to one
observation. |
knots |
A vector givint the coordinates of the knots. |
degree |
The degree of the penalized smoothing spline. |
plot |
Logical. If TRUE (default), the evolution of the CV
score as the penalty increases is plotted. |
n.points |
A numeric giving the number of CV computations needed to produce the plot. |
... |
Options to be passed to the nlm
function. |
For every linear smoother e.g. y.hat = S_lambda y, the cross-validation criterion consists in minimizing the following quantity:
CV(lambda) = sum_{i=1}^n [(y_i - y.hat_i) / (1 - S_{lambda,ii}) ]^2
where lambda is the penalty coefficient, n the number of observations and S_{lambda,ii} the i-th diagonal element of the matrix S_lambda.
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 link{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.
n <- 200 x <- runif(n) fun <- function(x) sin(3 * pi * x) y <- fun(x) + rnorm(n, 0, sqrt(0.4)) knots <- quantile(x, prob = 1:(n/4) / (n/4 + 1)) cv(y, x, knots = knots, degree = 3)