iterchoiceS1cv {ibr}R Documentation

Selection of the number of iterations for iterative bias reduction smoothers with base thin-plate splines smoother

Description

The function iterchoiceS1cv searches the interval from mini to maxi for a minimum of the function criterion with respect to its first argument using optimize. This function is not intended to be used directly.

Usage

iterchoiceS1cv(X, y, lambda, df, ddlmini, ntest, ntrain,
Kfold, type, npermut, seed, Kmin, Kmax, criterion, m,
fraction)

Arguments

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.
criterion The criteria available are map ("map") or rmse ("rmse").
m The order of thin plate splines. This integer m must verifies 2m/d>1, where d is the number of explanatory variables.
fraction The subdivision of the interval [Kmin,Kmax].

Value

Returns the optimum number of iterations (between Kmin and Kmax).

Author(s)

Pierre-Andre Cornillon, Nicolas Hengartner and Eric Matzner-Lober.

References

Cornillon, P. A., Hengartner, N. and Matzner-Lober, E. (2009) Recursive Bias Estimation for high dimensional regression smoothers. submitted.

See Also

ibr


[Package ibr version 1.2 Index]