Features of “PAD” (“Permutaciones, Analisis Discriminante”)
 Performs Discriminant analyses with limited graphical display (button “Mapa factorial“)
 Computation is based on the inverse of the consensus covariance matrix
 Factor map of the first two discriminant factors (DF1,DF2) only
 Respective contributions of each discriminant factor
 Table of Mahalanobis distances, formatted as for PHYLIP (J. Felsenstein) tree construction.
 Permutation of individuals among groups (button “Permutar distancias ?“)
 Random permutation of individuals in the total sample is used as a method to randomly interchange individuals among groups
 At each iteration, a complete discriminant analysis is performed
 As an estimate of statistical significance for each Mahalanobis distance, a proportion is given which is the number of Mahalanobis distances values equal or higher than observed, relative to the total number of permutations.
 Sizefree analysis (button “DISTANCES, or TRADITIONAL“)
 Input data MUST be logtransformed measurements
 Sizefree variables are computed (row centered data)
 Principal components are derived from these sizefree variables, called here “sizefree components”
 The optimal number of sizefree components is suggested in relation to local sample sizes
 A discriminant analysis is performed on this number of sizefree components.
 Allometric residual may be examined on each of the two first discriminant factors.
 Coordinates of landmarks analysis (button “LANDMARKS, or GEOMETRIC“)
 Input data are always Partial Warps, they are used either without restriction (button “PW, option ‘Total’) or “partly” (button “PW, option ‘Part’), meaning that only a few first Relative Warps are used. Select that option when the number of PW is too high relative to the number of individuals in the smallest group.
 Thus, if the option “PW, option ‘Part’ is selected, Relative Warps are computed and a subset of them is suggested for subsequent analyses.
 Allometric residual may be examined on each of the two first discriminant factors, provided a file is opened containing corresponding ‘centroid sizes’ (CS).
 Supplementary data (button “Datos Externos“)
 External data may be called, contained in a FORMAT formatted file.
 Their position on the DF1/DF2 factor map is computed according to the first two discriminant factors, DF1 and DF2
 In case of two groups, supplementary data are visible on the histogram of discriminating classes(March 2009)
 Mahalanobis distances are computed between each external individual and each centroid of the discriminant model
 A classification table is given
 This procedure is NOT available anymore for PW data; for a shape based Mahalanobis classification of unknown individuals relative to 2 or more groups, please use the MOG module.
 Reclassification of individuals (button “C“)
 This corresponds to the same sample being treated as external data
 Cross checked reclassification (button “CCC“).
 For each individual classification the individual itself is removed from the total sample, then:
– sample sizes are computed again;
– the Mahalanobis distances are computed between new centroids;
– the removed individual is then used as supplementary data, and its distance computed with each centroid;
– a reclassification table is contructed.
 External softwareis available (menu)
 The PHYLIP ‘neighbor’ and ‘fitch’ modules (J. Felsenstein) may be called directly from PAD; people wanting to use other modules of the PHYLIP package should go here.
 After answering the questions by the letters ‘N‘ (to obtain the UPGMA method instead of the NeighborJoigning one), ‘L‘ (lower diagoal matrix) and ‘Y‘ (yes) …
 … the PHYLIP outfile is directly printed within the PAD report.
 Each OTU is labeled as a number according to its order position in the input file. Giving a name to each OTU is possible by the menu “External Software” > “Giving a name to each OTU”
 Direct access is also given to an editing tree software, njplot (see: Perriere, G. and Gouy, M. (1996) WWWQuery: An online retrieval system for biological sequence banks. Biochimie, 78, 364369).
