iterchoiceA {ibr} | R Documentation |
Selection of the number of iterations for iterative bias reduction smoothers
Description
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.
Usage
iterchoiceA(n, mini, maxi, eigenvaluesA, tPADmdemiY, DdemiPA,
ddlmini, ddlmaxi, y, criterion, fraction)
Arguments
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 [ |
Details
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.
Value
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).
Author(s)
Pierre-Andre Cornillon, Nicolas Hengartner and Eric Matzner-Lober.
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.
Cornillon, P.-A.; Hengartner, N. and Matzner-Lober, E. (2017) Iterative Bias Reduction Multivariate Smoothing in R: The ibr Package. Journal of Statistical Software, 77, 1–26.