Programmer Guide/Command Reference/EVAL/modclust: Difference between revisions
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:;<var>alpha</var>:optional factor for BIC calculation (default=1); 0 <= ''alpha'' <= 100 | :;<var>alpha</var>:optional factor for BIC calculation (default=1); 0 <= ''alpha'' <= 100 | ||
;Result 1: On return the hierarchy information is stored in ''htable'' (Nx3 matrix) and the BIC values are stored in ''bictable'' ((''max''-''min''+1)x3 matrix). The return value ''ibest'' is the index of the BIC table entry with the highest BIC value (<code>''ibtest''=imax(''bictable''[*,2]</code>). | ;Result 1: On return the hierarchy information is stored in ''htable'' (Nx3 matrix) and the BIC values are stored in ''bictable'' ((''max''-''min''+1)x3 matrix). The return value ''ibest'' is the index of the BIC table entry with the highest BIC value (<code>''ibtest''=imax(''bictable''[*,2]</code>). | ||
: | :Format of the hierarchy table ''htable'': N rows, 3 columns | ||
::{|class="einrahmen" | ::{|class="einrahmen" | ||
|column 0 ||index of min. row (from) | |column 0 ||index of min. row (from) | ||
| Line 27: | Line 27: | ||
|column 2 ||agglomeration cost (distance) | |column 2 ||agglomeration cost (distance) | ||
|} | |} | ||
: | :Format of the BIC table ''bictable'': ''max''-''min''+1 rows, 3 columns | ||
::{|class="einrahmen" | ::{|class="einrahmen" | ||
|column 0 ||number of clusters | |column 0 ||number of clusters | ||
Revision as of 11:22, 21 April 2011
Model based agglomerative clustering. A hierarchy and BIC (Bayesian Information Criterion) values are calculated for given data vectors.
- Usage 1
modclust(x, htable, bictable, mflag {, min, max, alpha})- x
- data matrix NxM, one data vector with length M per row
- htable
- hierarchy table (reference used for output)
- bictable
- BIC table (reference used for output)
- mflag
- method for distance and BIC calculation
mflag method 0 Single Linkage 1 Complete Linkage (linaer distances) 2 Complete Linkage (log. distances)
- min, max
- optional minimum (default=2) and maximum (default=N) number of clusters for BIC calculation; 2 <= min < max <= N
- alpha
- optional factor for BIC calculation (default=1); 0 <= alpha <= 100
- Result 1
- On return the hierarchy information is stored in htable (Nx3 matrix) and the BIC values are stored in bictable ((max-min+1)x3 matrix). The return value ibest is the index of the BIC table entry with the highest BIC value (
ibtest=imax(bictable[*,2]). - Format of the hierarchy table htable: N rows, 3 columns
column 0 index of min. row (from) column 1 index of min. column (to) column 2 agglomeration cost (distance)
- Format of the BIC table bictable: max-min+1 rows, 3 columns
column 0 number of clusters column 1 log. likelihood column 2 BIC
- Usage 2
modclust(htable, nclust)- htable
- cluster hierarchy table (Nx3 matrix, see Usage 1).
- nclust
- number of clusters
- Result 2
- The created partition table ptable, which is a vector with N elements containing the group indices. The value ptable[i] (i=0..N) is the index of the cluster containing the data vector i.
- Usage 3
modclust(ptable, iclust { x})- ptable
- partition table (Nx1 matrix, see Usage 2).
- nclust
- index of cluster to be extracted
- x
- input data matrix (NxM matrix, see Usage 1)
- Result 3
-
- if x is supplied: matrix of all data vectors (rows of x) associated with the cluster iclust
- otherwise: index vector containing the indices of the data vectors associated with cluster iclust
