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ibr (version 2.0-2)

iterchoiceAcv: Selection of the number of iterations for iterative bias reduction smoothers

Description

The function iterchoiceAcv 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

iterchoiceAcv(X, y, bx, df, kernelx, ddlmini, ntest, ntrain, Kfold,
type, npermut, seed, Kmin, Kmax, criterion, 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.
bx
The vector of different bandwidths, length $p$.
df
A numeric vector of either length 1 or length equal to the number of columns of x. If smoother="k", it indicates the desired effective degree of freedom (trace) of the smoothing matrix for each variable ; df is repeated when the length of vector df is 1. This argument is useless if bandwidth is supplied (non null).
kernelx
Character string which allows to choose between gaussian kernel ("g"), Epanechnikov ("e"), uniform ("u"), quartic ("q"). The default (gaussian kernel) is strongly advised.
ddlmini
The number of eigenvalues (numerically) 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").
fraction
The subdivision of the interval [Kmin,Kmax].

Value

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

References

Cornillon, P.-A.; Hengartner, N.; Jegou, N. and Matzner-Lober, E. (2012) Iterative bias reduction: a comparative study. Statistics and Computing, 23, 777-791.

Cornillon, P.-A.; Hengartner, N. and Matzner-Lober, E. (2013) Recursive bias estimation for multivariate regression smoothers Recursive bias estimation for multivariate regression smoothers. ESAIM: Probability and Statistics, 18, 483-502.

See Also

ibr