Description Usage Arguments Value Author(s) References See Also
Evaluates at each iteration proposed in the grid the crossvalidated 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.
1 2  iterchoiceS1cve(X, y, lambda, df, ddlmini, ntest, ntrain,
Kfold, type, npermut, seed, Kmin, Kmax, m, s)

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. 
lambda 
A numeric positive coefficient that governs the amount of penalty (coefficient lambda). 
df 
A numeric vector of length 1 which is multiplied by the minimum df of thin
plate splines ; This argument is useless if

ddlmini 
The number of eigenvalues 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. 
m 
The order of derivatives for the penalty (for thin plate splines it is the order). This integer m must verify 2m+2s/d>1, where d is the number of explanatory variables. 
s 
The power of weighting function. For thin plate splines s is equal to 0. This real must be strictly smaller than d/2 (where d is the number of explanatory variables) and must verify 2m+2s/d. To get pseudocubic splines, choose m=2 and s=(d1)/2 (See Duchon). 
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
.
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.
Duchon, J. (1977) Splines minimizing rotationinvariant seminorms in Solobev spaces. in W. Shemp and K. Zeller (eds) Construction theory of functions of several variables, 85100, Springer, Berlin.
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