iterchoiceAcv {ibr} | R Documentation |
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)
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 ]. |
Returns the optimum number of iterations (between Kmin
and Kmax
).
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.