ibr (version 2.0-2)

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

Description

Evaluates at each iteration proposed in the grid the cross-validated root mean squared error (RMSE) and mean of the relative absolute error (MAP). The minimum of these criteria gives an estimate of the optimal number of iterations. This function is not intended to be used directly.

Usage

iterchoiceAcve(X, y, bx, df, kernelx, ddlmini, ntest, ntrain,
Kfold, type, npermut, seed, Kmin, Kmax)

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.

Value

Returns the values of RMSE and MAP for each value of the grid K. Inf are returned if the iteration leads to a smoother with a df bigger than ddlmaxi.

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