Automatically compute different clustering solutions and associated quality measures to help identifying the best one.

1 2 3 4 5 6 7 8 9 | ```
wcCmpCluster(diss, weights = NULL, maxcluster, method = "all", pam.combine = TRUE)
## S3 method for class 'clustrangefamily'
print(x, max.rank=1, ...)
## S3 method for class 'clustrangefamily'
summary(object, max.rank=1, ...)
## S3 method for class 'clustrangefamily'
plot(x, group="stat", method="all", pam.combine=FALSE,
stat="noCH", norm="none", withlegend=TRUE, lwd=1, col=NULL, legend.prop=NA,
rows=NA, cols=NA, main=NULL, xlab="", ylab="", ...)
``` |

`diss` |
A dissimilarity matrix or a dist object (see |

`weights` |
Optional numerical vector containing weights. |

`maxcluster` |
Integer. Maximum number of cluster. The range will include all clustering solution starting from two to |

`method` |
A vector of hierarchical clustering methods to compute or |

`pam.combine` |
Logical. Should we try all combinations of hierarchical and PAM clustering? |

`x` |
A |

`object` |
A |

`max.rank` |
Integer. The different number of solution to print/summarize |

`group` |
One of |

`stat` |
Character. The list of statistics to plot or "noCH" to plot all statistics except "CH" and "CHsq" or "all" for all statistics. See |

`norm` |
Character. Normalization method of the statistics can be one of "none" (no normalization), "range" (given as (value -min)/(max-min), "zscore" (adjusted by mean and standard deviation) or "zscoremed" (adjusted by median and median of the difference to the median). |

`withlegend` |
Logical. If |

`lwd` |
Numeric. Line width, see |

`col` |
A vector of line colors, see |

`legend.prop` |
When |

`rows,cols` |
optional arguments to arrange plots. |

`xlab` |
x axis label. |

`ylab` |
y axis label. |

`main` |
main title of the plot. |

`...` |
Additionnal parameters passed to |

An object of class `clustrangefamily`

with the following elements:

- Method name:
the results of

`as.clustrange`

objects under each method name (see argument`method`

for a list of possible values)`allstats`

:A

`matrix`

containing the clustering statistics for each cluster solution and method.`param`

:The parameters set when the function was called.

See Also `as.clustrange`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | ```
data(mvad)
#Creating state sequence object
mvad.seq <- seqdef(mvad[, 17:86])
# COmpute distance using Hamming distance
diss <- seqdist(mvad.seq, method="HAM")
#Ward clustering
allClust <- wcCmpCluster(diss, maxcluster=15, method=c("average", "pam", "beta.flexible"),
pam.combine=FALSE)
summary(allClust, max.rank=3)
##Plot PBC, RHC and ASW
plot(allClust, stat=c("PBC", "RHC", "ASW"), norm="zscore", lwd=2)
##Plot PBC, RHC and ASW grouped by cluster method
plot(allClust, group="method", stat=c("PBC", "RHC", "ASW"), norm="zscore", lwd=2)
``` |

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