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.
iterchoiceA(n, mini, maxi, eigenvaluesA, tPADmdemiY, DdemiPA,
ddlmini, ddlmaxi, y, criterion, fraction)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).
The number of observations.
The lower end point of the interval to be searched.
The upper end point of the interval to be searched.
Vector of the eigenvalues of the symmetric matrix A.
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.
The square root of the diagonal matrix D times the eigen vectors of the symmetric matrix A.
The number of eigenvalues (numerically) equals to 1.
The maximum df. No criterion is calculated and
Inf is returned.
The vector of observations of dependant variable.
The criteria available are GCV (default, "gcv"),
AIC ("aic"), corrected AIC ("aicc"), BIC
("bic") or gMDL ("gmdl").
The subdivision of the interval [mini,maxi].
Pierre-Andre Cornillon, Nicolas Hengartner and Eric Matzner-Lober.
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.
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.
ibr, iterchoiceA