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
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
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
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.
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,
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.
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
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