View source: R/coclusterContinuous.R
coclusterContinuous | R Documentation |
This function performs Co-Clustering (simultaneous clustering of rows and columns ) for continuous data-sets using latent block models. It can also be used to perform semi-supervised co-clustering.
coclusterContinuous(
data,
semisupervised = FALSE,
rowlabels = integer(0),
collabels = integer(0),
model = NULL,
nbcocluster,
strategy = coclusterStrategy(),
nbCore = 1
)
data |
Input data as matrix (or list containing data matrix.) | |||||||||||||||||||||
semisupervised |
Boolean value specifying whether to perform semi-supervised co-clustering or not. Make sure to provide row and/or column labels if specified value is true. The default value is false. | |||||||||||||||||||||
rowlabels |
Vector specifying the class of rows. The class number starts from zero. Provide -1 for unknown row class. | |||||||||||||||||||||
collabels |
Vector specifying the class of columns. The class number starts from zero. Provide -1 for unknown column class. | |||||||||||||||||||||
model |
This is the name of model. The following models exists for Gaussian data:
| |||||||||||||||||||||
nbcocluster |
Integer vector specifying the number of row and column clusters respectively. | |||||||||||||||||||||
strategy |
Object of class | |||||||||||||||||||||
nbCore |
number of thread to use (OpenMP must be available), 0 for all cores. Default value is 1. |
Return an object of BinaryOptions
or ContingencyOptions
or ContinuousOptions
depending on whether the data-type is Binary, Contingency or Continuous
respectively.
# Simple example with simulated continuous data
#load data
data(gaussiandata)
#usage of coclusterContinuous function in its most simplest form
out<-coclusterContinuous(gaussiandata,nbcocluster=c(2,3))
#Summarize the output results
summary(out)
#Plot the original and Co-clustered data
plot(out)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.