Description Usage Arguments Value Author(s) References See Also
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.
1 2  iterchoiceAcv(X, y, bx, df, kernelx, ddlmini, ntest, ntrain, Kfold,
type, npermut, seed, Kmin, Kmax, criterion, fraction)

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 
kernelx 
Character string which allows to choose between gaussian kernel
( 
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 
type 
A character string in

npermut 
The number of random draw (with replacement), used for

seed 
Controls the seed of random generator
(via 
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 ( 
fraction 
The subdivision of the interval [ 
Returns the optimum number of iterations (between Kmin
and Kmax
).
PierreAndre Cornillon, Nicolas Hengartner and Eric MatznerLober.
Cornillon, P.A.; Hengartner, N.; Jegou, N. and MatznerLober, E. (2012) Iterative bias reduction: a comparative study. Statistics and Computing, 23, 777791.
Cornillon, P.A.; Hengartner, N. and MatznerLober, E. (2013) Recursive bias estimation for multivariate regression smoothers Recursive bias estimation for multivariate regression smoothers. ESAIM: Probability and Statistics, 18, 483502.
Cornillon, P.A.; Hengartner, N. and MatznerLober, E. (2017) Iterative Bias Reduction Multivariate Smoothing in R: The ibr Package. Journal of Statistical Software, 77, 1–26.
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