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.
iterchoiceAcv(X, y, bx, df, kernelx, ddlmini, ntest, ntrain, Kfold,
type, npermut, seed, Kmin, Kmax, criterion, fraction)Returns the optimum number of iterations (between Kmin and Kmax).
A numeric matrix of explanatory variables, with n rows and p columns.
A numeric vector of variable to be explained of length n.
The vector of different bandwidths, length \(p\).
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).
Character string which allows to choose between gaussian kernel
("g"), Epanechnikov ("e"), uniform ("u"),
quartic ("q"). The default (gaussian kernel) is strongly advised.
The number of eigenvalues (numerically) equals to 1.
The number of observations in test set.
The number of observations in training set.
Either the number of folds or a boolean or NULL.
A character string in
random,timeseries,consecutive, interleaved
and give the type of segments.
The number of random draw (with replacement), used for
type="random".
Controls the seed of random generator
(via set.seed).
The minimum number of bias correction iterations of the search grid considered by the model selection procedure for selecting the optimal number of iterations.
The maximum number of bias correction iterations of the search grid considered by the model selection procedure for selecting the optimal number of iterations.
The criteria available are map ("map") or rmse
("rmse").
The subdivision of the interval [Kmin,Kmax].
Pierre-Andre Cornillon, Nicolas Hengartner and Eric Matzner-Lober.
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.
Cornillon, P.-A.; Hengartner, N. and Matzner-Lober, E. (2017) Iterative Bias Reduction Multivariate Smoothing in R: The ibr Package. Journal of Statistical Software, 77, 1--26.
ibr