Build a clustrange object to compare different clustering solutions.

1 2 3 4 5 6 7 8 9 | ```
as.clustrange(object, diss, weights=NULL, R=1, samplesize=NULL, ...)
## S3 method for class 'twins'
as.clustrange(object, diss, weights=NULL, R=1, samplesize=NULL, ncluster=20, ...)
## S3 method for class 'hclust'
as.clustrange(object, diss, weights=NULL, R=1, samplesize=NULL, ncluster=20, ...)
## S3 method for class 'clustrange'
plot(x, stat="noCH", legendpos="bottomright",
norm="none", withlegend=TRUE, lwd=1, col=NULL, ylab="Indicators",
xlab="N clusters", conf.int=0.9, ci.method="none", ci.alpha=.3, line="t0", ...)
``` |

`object` |
The object to convert such as a data.frame. |

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

`weights` |
Optional numerical vector containing weights. |

`R` |
Optional number of bootstrap that can be used to build confidence intervals. |

`samplesize` |
Size of bootstrap sample. Default to sum of weights. |

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

`x` |
A |

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

`legendpos` |
Character. legend position, 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 |

`xlab` |
x axis label. |

`ylab` |
y axis label. |

`conf.int` |
Confidence to build the confidence interval (default: 0.9). |

`ci.method` |
Method used to build the confidence interval (only if bootstrap has been used, see R above). One of "none" (do not plot confidence interval), "norm" (based on normal approximation), "perc" (based on percentile).) |

`ci.alpha` |
alpha color value used to plot the interval. |

`line` |
Which value should be plotted by the line? One of "t0" (value for actual sample), "mean" (average over all bootstraps), "median"(median over all bootstraps). |

`...` |
Additionnal parameters passed to/from methods. |

`as.clustrange`

convert objects to `clustrange`

objects. `clustrange`

objects contains a list of clustering solution with associated statistics and can be used to find the optimal clustering solution.

If `object`

is a `data.frame`

or a `matrix`

, each column should be a clustering solution to be evaluated.

If `object`

is an `hclust`

or `twins`

objects (i.e. hierarchical clustering output, see `hclust`

, `diana`

or `agnes`

), the function compute all clustering solution ranging from two to `ncluster`

and compute the associated statistics.

An object of class `clustrange`

with the following elements:

`clustering`

:A

`data.frame`

of all clustering solutions.`stats`

:A

`matrix`

containing the clustering statistics of each cluster solution.

See also `wcKMedRange`

, `wcClusterQuality`

.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | ```
data(mvad)
## Aggregating state sequence
aggMvad <- wcAggregateCases(mvad[, 17:86], weights=mvad$weight)
## Creating state sequence object
mvad.seq <- seqdef(mvad[aggMvad$aggIndex, 17:86], weights=aggMvad$aggWeights)
## COmpute distance using Hamming distance
diss <- seqdist(mvad.seq, method="HAM")
## Ward clustering
wardCluster <- hclust(as.dist(diss), method="ward", members=aggMvad$aggWeights)
## Computing clustrange from Ward clustering
wardRange <- as.clustrange(wardCluster, diss=diss,
weights=aggMvad$aggWeights, ncluster=15)
## Plot all statistics (standardized)
plot(wardRange, stat="all", norm="zscoremed", lwd=3)
## Plot HC, RHC and ASW
plot(wardRange, stat=c("HC", "RHC", "ASWw"), norm="zscore", lwd=3)
``` |

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