W

wardcont

performes Ward's hierarchical cluster analysis of the data of the rows and the columns of a contingency table including the multivariate graphic using the correspondence analysis; makes the factorial coordinates of the points in a row and in a column available

warning

warning enables the user to send warning messages out of quantlets. There are displayed when the whole program finishes.

waveint1b

Shows the mother wavelet for a given basis. In the picture of the first row the function is shown. Below, in the second row, the mother wavelet coefficients are shown. For each level in a horizontal line are vertical lines plotted. The length of the vertical line depends on the size of the wavelet

waveint2b

Shows the mother wavelet for a given basis. In the picture of the first row the function is shown. Below, in the second row, the coeffients are drawn as circles in one line for each level. The absolute size is indicated by the radius of the circle. If the circle is red the coefficient is used in th

waveint3b

Shows the mother wavelet for a given basis. In the picture of the first row the function is shown. Below, in the second row, the coeffients are drawn as vertical lines. All coefficients are ordered by absolute size. Just one quarter of all coefficients is drawn.

waveint4b

Shows the mother wavelet for a given basis. In the picture of the first row the function is shown. Below, in the second row, we see as lowest function the approximation with only using the father wavelet coefficients. Then we add the first level of mother wavelet coefficients to the approximation,

waveint5b

Shows the wavelet coefficients for a given basis. In the picture of the first row the bivariate image is shown. Below, in the second row, we see the 32 absolute largest father/mother coeffients ordered by size.

waveletmain

Generates global objects which contain the wavelet coefficients

wavelettest

tests all noninteractive quantlets of wavelet.lib

wavesmo

Generates smoothed mother wavelet coeffients and the resulting estimate. x contains the vector of data and l gives the number of father wavelet coefficient (power of 2). h is the vector of wavelet basis coeffients (automatically generated by calling the quantlib wavelet). s contains the threshold v

wavetest

Additive component analysis in additive separable models using wavelet estimation. The first (additive) component is tested against a given polynomial form of degree p, e.g., if p=1 is to test linearity, p=0 is to test for significant influence of the first component at all etc. For details see Hae

weidist1

transforms weights in distances (\delta^{(2)})

weidist2

transforms weights in distances (\delta^{(2)})

weidist3

transforms weights in distances

weinit

initializes the weights of a neural network

while

while begins a loop if the first element of the argument is true. while has to be followed by an endo.

wilcoxon2

performs the two-sample Wilcoxon test

wilgenfun

calculates coefficients of the generating function of Wilcoxon test statistic

write

write writes an array into a file.

writecomponent

internal routine for writelist

writecsv

writes numerical and text data to a CSV file

writelist

Writes a composed object as ASCII data to a set of files. All elements of the composed object have to be numerical matrices or textvectors !

writem

writes numerical and text data to a text file

wtsder

computes the weights for derivative estimation for the use with the quatric kernel in the context of binning