A hierarchical clustering dendorgram such as one returned by hclust.

Maximum joining heights that will be considered. For method=="tree" it defaults to 0.99. For method=="hybrid" it defaults to 99% of the range between the 5th percentile and the maximum of the joining heights on the dendrogram.

Minimum cluster size.

Chooses the method to use. Recognized values are "hybrid" and "tree".

Only used for method "hybrid". The distance matrix used as input to hclust. If not given and method == "hybrid", the function will issue a warning and default to method = "tree".

For method "hybrid", can be either logical or integer in the range 0 to 4. For method "tree", must be logical. In both cases, provides a rough control over sensitivity to cluster splitting. The higher the value (or if TRUE), the more and smaller clusters will be produced. For the "hybrid" method, a finer control can be achieved via maxCoreScatter and minGap below.

Only used for method "hybrid". Maximum scatter of the core for a branch to be a cluster, given as the fraction of cutHeight relative to the 5th percentile of joining heights. See Details.

Only used for method "hybrid". Minimum cluster gap given as the fraction of the difference between cutHeight and the 5th percentile of joining heights.

Only used for method "hybrid". Maximum scatter of the core for a branch to be a cluster given as absolute heights. If given, overrides maxCoreScatter.

Only used for method "hybrid". Minimum cluster gap given as absolute height difference. If given, overrides minGap.

Minimum split height given as the fraction of the difference between cutHeight and the 5th percentile of joining heights. Branches merging below this height will automatically be merged. Defaults to zero but is used only if minAbsSplitHeight below is NULL.

Minimum split height given as an absolute height. Branches merging below this height will automatically be merged. If not given (default), will be determined from minSplitHeight above.

Optional function to evaluate split (dissimilarity) between two branches. Either a single function or a list in which each component is a function (see assumeSimpleExternalSpecification below for how to specify a single function). Each function can be specified by name (a character string) or the actual function object. Each given function must take arguments branch1 and branch2 that specify the indices of objects in the two branches whose dissimilarity is to be evaluated, and possibly other arguments. It must return a number that quantifies the dissimilarity of the two branches. The returned value will be compared to minExternalSplit (see below). This argument is only used for method "hybrid".

Thresholds to decide whether two branches should be merged. It should be a numeric vector of the same length as the number of functions in externalBranchSplitFnc above. Only used for method "hybrid".

Further arguments to function externalBranchSplitFnc. If only one external function is specified in externalBranchSplitFnc above, externalSplitOptions can be a named list of arguments or a list with one component that is in turn the named list of further arguments for externalBranchSplitFnc[[1]]. The argument assumeSimpleExternalSpecification controls which of the two possibilities should be assumed. If multiple functions are specified in externalBranchSplitFnc, externalSplitOptions must be a list in which each component is a named list giving the further arguments for the corresponding function in externalBranchSplitFnc. Only used for method "hybrid".

Optional specification of whether the external branch split functions need the distance matrix as one of their arguments. Either NULL or a logical vector with one element per branch split function that specifies whether the corresponding branch split function expects the distance matrix as one of its arguments. The default NULL is interpreted as a vector of TRUE. When dealing with a large number of objects, setting this argument to FALSE whenever possible can prevent unnecessary memory utilization.

Logical: when minExternalSplit above is a scalar (has length 1), should the function assume a simple specification of externalBranchSplitFnc and externalSplitOptions? If TRUE, externalBranchSplitFnc is taken as the function specification and externalSplitOptions the named list of options. This is suitable for simple direct calls of this function. If FALSE, externalBranchSplitFnc is assumed to be a list with a single component which specifies the function, and externalSplitOptions is a list with one component that is in turn the named list of further arguments for externalBranchSplitFnc[[1]].

Only used for method "hybrid". If TRUE, the second (PAM-like) stage will be performed.

Logical, only used for method "hybrid". If TRUE, the PAM stage will respect the dendrogram in the sense that objects and small clusters will only be assigned to clusters that belong to the same branch that the objects or small clusters being assigned belong to.

Only used for method "hybrid" and only if labelUnlabeled==TRUE. If TRUE, the second stage will be use object to medoid distance; if FALSE, it will use average object to cluster distance. The default (FALSE) is recommended.

Deprecated, use maxPamDist instead. Only used for method "hybrid" and only if labelUnlabeled==TRUE. Maximum object distance to closest cluster that will result in the object assigned to that cluster.

Only used for method "hybrid" and only if labelUnlabeled==TRUE. Maximum object distance to closest cluster that will result in the object assigned to that cluster. Defaults to cutHeight.

Only used for method "hybrid" and only if labelUnlabeled==TRUE. If TRUE, branches that failed to be clusters in stage 1 only because of insufficient size will be assigned together in stage 2. If FALSE, all objects will be assigned individually.

Controls the verbosity of the output. 0 will make the function completely quiet, values up to 4 gradually increase verbosity.

Controls indentation of printed messages (see verbose above). Each unit adds two spaces before printed messages; useful when several functions' output is to be nested.