Description Usage Arguments Details Value Note References Examples
Testing the significance of each monothetic clustering split by permutation
methods. The "simplewithhold" method ("sw"
) shuffles the observations
between two groups without the splitting variable. The other two methods
shuffle the values in the splitting variable to create a new data set, then
it either splits again on that variable ("resplitlimit", "rl"
) or use all
variables as the splitting candidates ("resplitnolimit", "rn"
).
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object 
The 
data 
The data set which is being clustered. 
auto.pick 
Whether the algorithm stops when pvalue becomes larger than

sig.val 
Significance value to decide when to stop splitting. This
option is ignored if 
method 
Can be chosen between 
rep 
Number of permutations required to calculate test statistic. 
stat 
Statistic to use. Choosing between 
bon.adj 
Whether to adjust for multiple testing problem using Bonferroni correction. 
ncores 
Number of CPU cores on the current host. When set to NULL, all available cores are used. 
The stat
calculated from the shuffles create the reference distribution
to find the pvalue. Because the splitting variable that was chosen is
already the best in terms of reduction of inertia, that variable is withheld
from the distance matrix used in the permutation test.
This method shuffles the values of the splitting variables while keeping
other variables fixed to create a new data set, then the chosen stat
is
calculated for each rep to compare with the observed stat
.
Similar to Method 2 but all variables are splitting candidates.
A hypothesis test occurred lower in the monothetic clustering tree could have its pvalue corrected for multiple tests happened before it in order to reach that node. The formula is
adj.p = unadj.p \times depth,
with depth is 1 at the root node.
The same MonoClust
object with an extra column (pvalue), as well
as the numofclusters
object if auto.pick = TRUE
.
This function uses foreach::foreach()
to facilitate parallel
processing. It distributes reps to processes.
Calinski, T. and Harabasz, J (1974). "A dendrite method for cluster analysis". en. In: Communications in Statistics 3.1, pp. 127. doi: 10.1080/03610927408827101.
Rousseeuw, P. J. (1987). "Silhouettes: A graphical aid to the interpretation and validation of cluster analysis". In: Journal of Computational and Applied Mathematics 20, pp. 5365. ISSN: 03770427. doi: 10.1016/03770427(87)901257.
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