Description Usage Arguments Details Value Author(s) References See Also
The function iterchoiceA
searches the interval from
mini
to maxi
for a minimum of the function
which calculates the chosen
criterion
(critAgcv
, critAaic
, critAbic
,
critAaicc
or critAgmdl
) with respect to its first
argument (a given iteration k
) using optimize
. This function is not
intended to be used directly.
1 2  iterchoiceA(n, mini, maxi, eigenvaluesA, tPADmdemiY, DdemiPA,
ddlmini, ddlmaxi, y, criterion, fraction)

n 
The number of observations. 
mini 
The lower end point of the interval to be searched. 
maxi 
The upper end point of the interval to be searched. 
eigenvaluesA 
Vector of the eigenvalues of the symmetric matrix A. 
tPADmdemiY 
The transpose of the matrix of eigen vectors of the symmetric matrix A times the inverse of the square root of the diagonal matrix D. 
DdemiPA 
The square root of the diagonal matrix D times the eigen vectors of the symmetric matrix A. 
ddlmini 
The number of eigenvalues (numerically) equals to 1. 
ddlmaxi 
The maximum df. No criterion is calculated and

y 
The vector of observations of dependant variable. 
criterion 
The criteria available are GCV (default, 
fraction 
The subdivision of the interval [ 
See the reference for detailed explanation of A and
D. The interval [mini
,maxi
] is splitted into
subintervals using fraction
. In each subinterval the function
fcriterion
is minimzed using optimize
(with respect
to its first argument) and the minimum (and its argument) of the
result of these optimizations is returned.
A list with components iter
and objective
which give the
(rounded) optimum number of iterations (between
Kmin
and Kmax
) and the value
of the function at that real point (not rounded).
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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