corclust {klaR} | R Documentation |
A hierarchical clustering of variables using hclust
is performed using
1 - the absolute correlation as a distance measure between tow variables.
corclust(x, cl = NULL, mincor = NULL, prnt = FALSE, method = "complete")
x |
Either a data frame or a matrix consisting of numerical attributes. |
cl |
Optional vector of ty factor indicating class levels, if class specific correlations should to be considered. |
mincor |
Optional vector of degrees of correlation within a cluster of variables that will be indicated in the plot by a line. |
prnt |
Logical indicating whether the matrix of distances should be printed. |
method |
Linkage to be used for clustering. Default is complete linkage. |
The main output consists in the tree visualization of the clustered variables.
Each cluster consists of a set of correlated variables according to the chosen clustering criterion.
The default criterion is ‘complete
’. This choice is meaningful as it represents the
minimum absolute correlation between all variables of a cluster.\
Further proceeding would consist in chosing one variable of each cluster to obtain a
subset of rather uncorrelated variables for further analysis.\
If an additional class vector cl
is given to the function for any two variables their minimum
correlation over all classes is used.
min.abs.cor |
Matrix of distances used for clustering containing 1 - the absolute correlation between any two variables. |
clustering |
Result object of the hierarchical clustering. |
Gero Szepannek
See also hclust
, for details on the clustering algorithm.
data(iris) classes <- iris$Species variables <- iris[,1:4] corclust(variables, classes, mincor = 0.6)