iterchoiceS1cve {ibr} | R Documentation |
Evaluates at each iteration proposed in the grid the cross-validated root mean squared error (RMSE) and mean of the relative absolute error (MAP). The minimum of these criteria gives an estimate of the optimal number of iterations. This function is not intended to be used directly.
iterchoiceS1cve(X, y, lambda, df, ddlmini, ntest, ntrain, Kfold, type, npermut, seed, Kmin, Kmax, m)
X |
A numeric matrix of explanatory variables, with n rows and p columns. |
y |
A numeric vector of variable to be explained of length n. |
lambda |
A numeric positive coefficient that governs the amount of penalty (coefficient lambda). |
df |
A numeric vector of length 1 which is multiplied by the minimum df of thin
plate splines ; This argument is useless if
lambda is supplied (non null). |
ddlmini |
The number of eigenvalues equals to 1. |
ntest |
The number of observations in test set. |
ntrain |
The number of observations in training set. |
Kfold |
Either the number of folds or a boolean or NULL . |
type |
A character string in
random ,timeseries ,consecutive , interleaved
and give the type of segments. |
npermut |
The number of random draw (with replacement), used for
type="random" . |
seed |
Controls the seed of random generator
(via set.seed ). |
Kmin |
The minimum number of bias correction iterations of the search grid considered by the model selection procedure for selecting the optimal number of iterations. |
Kmax |
The maximum number of bias correction iterations of the search grid considered by the model selection procedure for selecting the optimal number of iterations. |
m |
The order of thin plate splines. This integer m must verifies 2m/d>1, where d is the number of explanatory variables. |
Returns the values of RMSE and MAP for each
value of the grid K
. Inf
are returned if the iteration leads
to a smoother with a df bigger than ddlmaxi
.
Pierre-Andre Cornillon, Nicolas Hengartner and Eric Matzner-Lober.
Cornillon, P. A., Hengartner, N. and Matzner-Lober, E. (2009) Recursive Bias Estimation for high dimensional regression smoothers. submitted.