coefficients
(Package: treethresh) :
Extracting and updating coefficients stored in wd or imwd objects
extract.coefficients extracts wavelet coefficient vectors (in case of wd) and coefficient matrices (in case of imwd), so that these can be thresholded by treethresh or wtthresh. update.coefficients re-inserts these vector or matrices into the wd or imwd objects, such that the inverse transform can be computed using the thresholded coefficients.
Extracts from a tree object of the classes treethresh or wtthresh the estimated value of the weight w for each data point.
● Data Source:
CranContrib
● Keywords: nonparametric, tree
● Alias: get.w, get.w.treethresh, get.w.wtthresh
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prune
(Package: treethresh) :
Prune a tree using cross-validation
Extracts an optimal subtree from a tree object of the classes treethresh or wtthresh. Contrary to subtree the values of the complexity parameter C does not need to be given, but is determined using cross-validation.
● Data Source:
CranContrib
● Keywords: nonparametric, tree
● Alias: prune, prune.treethresh, prune.wtthresh
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wavelet.threshold is a more user-friendly function for thresholding wavelet coefficients stored in an wd or imwd object. It combines the functions extract.coefficients, estimate.sdev (rescales the coefficients accordingly), treethresh or wtthresh, prune, thresh, and insert.coefficients
● Data Source:
CranContrib
● Keywords: nonparametric, tree
● Alias: wavelet.treethresh
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wtthresh
(Package: treethresh) :
Compute optimal thresholding partition for a sequence of linked arrays
This function carries out the joint thresholding algorithm described in section 5 of Evers and Heaton (2009). Though the function, in principle, can work any sequence of arrays, it is designed to work with blocks of wavelet coefficients. These can be extracted from an wd or imwd object using the function extract.coefficients.
● Data Source:
CranContrib
● Keywords: nonparametric, tree
● Alias: wtthresh
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get.t
(Package: treethresh) :
Extract estimated hard threshold
Extracts from a tree object of the classes treethresh or wtthresh the estimated value of the hard threshold t for each data point.
● Data Source:
CranContrib
● Keywords: nonparametric, tree
● Alias: get.t, get.t.treethresh, get.t.wtthresh
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