as.clustrange {WeightedCluster} | R Documentation |
Build a clustrange object to compare different clustering solutions.
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 'dtclust' as.clustrange(object, diss, weights=NULL, R=1, samplesize=NULL, ncluster=20, labels = TRUE, ...) ## 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 |
labels |
Logical. If |
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
.
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)