The class "clValid" contains the clustering results
and validation measures from the accompanying call to the function
clValid.
Objects can be created using the function clValid.
clusterObjs:Object of class "list". A list
containing the results from the clustering methods.
measures:Object of class "array". A
3-dimensional array which contains the
validation measures for the clustering results. The first
dimension indicates the validation measures, the second the number of
clusters, and the third the clustering methods.
measNames:Object of class "character". The
names of the validation measures.
clMethods:Object of class "character". A
character vector giving the clustering methods.
labels:Object of class "character". A
character vector giving the item (gene) labels.
nClust:Object of class "numeric". A numeric
vector giving the numbers of clusters
which were evaluated.
validation:Object of class "character". A character vector giving the type of
validation measures used, consisting of some combination of
"internal", "stability", or "biological".
metric:Object of class "character". The metric used to determine the distance
matrix.
method:Object of class "character". For
hierarchical clustering, the agglomeration method used.
neighbSize:Object of class "numeric". For internal validation, the neighborhood size used for the
connectivity measure.
annotation:Object of class "character or array
or list".
Either a character string naming the Bioconductor annotation
package for mapping genes to GO categories, or a list with the names of the functional classes
and the observations belonging to each class.
GOcategory:Object of class "character". For biological validation, gives which GO
categories to use for biological validation. Can be one of "BP",
"MF", "CC", or "all"
goTermFreq:Object of class "numeric". For the
BSI, what threshold frequency of GO terms to use for functional annotation.
call:Object of class "call". Gives the call
to clValid used to create the clValid object.
signature(object = "clValid"): Returns the
names of the clustering methods.
signature(object = "clValid"): Returns the
results from the clustering methods.
Additional arguments:
method = clMethods(object)The clustering method(s) to extract.
signature(object = "clValid"): Returns the
names of the validation measures.
signature(object = "clValid"): Returns
the validation measures.
Additional arguments:
measures = measNames(object)The validation measure(s) to extract.
signature(object = "clValid"): Returns the
numbers of clusters evaluated.
signature(object = "clValid"):
Returns the optimal value for each validation measure, along with
the corresponding clustering method and number of clusters.
Additional arguments:
measures = measNames(object)The validation measure(s) to extract.
signature(x = "clValid", y = "missing"): Plots
the validation measures.
Additional arguments:
measures=measNames(x)The validation measures to plot.
legend=TRUEIf TRUE provides a legend.
legendLoc="topright"The location of the legend.
main=NULLTitle of graph.
pch=NULLPlotting characters to use.
type="b"Type of plot.
ask=prod(par("mfcol")) < length(measures) &&
dev.interactive()Logical. If TRUE the user is
prompted before each plot.
signature(x = "clValid"): Print method for class
clValid.
signature(object = "clValid"): Same as print.
signature(object = "clValid"): Summary method
for class clValid.
Additional arguments:
digits = max(3,getOption("digits")-3)The number of significant digits to use.
Brock, G., Pihur, V., Datta, S. and Datta, S. (2008) clValid: An R Package for Cluster Validation Journal of Statistical Software 25(4) http://www.jstatsoft.org/v25/i04
Datta, S. and Datta, S. (2003) Comparisons and validation of statistical clustering techniques for microarray gene expression data. Bioinformatics 19(4): 459-466.
Datta, S. and Datta, S. (2006) Methods for evaluating clustering algorithms for gene expression data using a reference set of functional classes. BMC Bioinformatics 7:397. http://www.biomedcentral.com/1471-2105/7/397
Handl, J., Knowles, K., and Kell, D. (2005) Computational cluster validation in post-genomic data analysis. Bioinformatics 21(15): 3201-3212.
For a description of the function 'clValid' see clValid.
For help on the clustering methods see hclust and
kmeans in package stats,
kmeans in package stats,
agnes, clara, diana,
fanny, and pam in package cluster,
som in package kohonen, Mclust
in package mclust, and sota.
For additional help on the validation measures see
connectivity, dunn,
stability,
BHI, and
BSI.
# NOT RUN {
## to delete
library(clValid)
data(mouse)
## internal validation
express <- mouse[1:25,c("M1","M2","M3","NC1","NC2","NC3")]
rownames(express) <- mouse$ID[1:25]
intern <- clValid(express, 2:6, clMethods=c("hierarchical","kmeans","pam"),
validation="internal")
slotNames(intern)
## view results
intern
summary(intern)
optimalScores(intern)
plot(intern)
## Extract objects from slots
measures(intern)
hierClust <- clusters(intern,"hierarchical")
plot(hierClust)
measNames(intern)
nClusters(intern)
# }
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