coclusterContingency {blockcluster} | R Documentation |

## Co-Clustering function.

### Description

This function performs Co-Clustering (simultaneous clustering of rows and columns ) for Contingency data-sets using latent block models.It can also be used to perform semi-supervised co-clustering.

### Usage

```
coclusterContingency(
data,
semisupervised = FALSE,
rowlabels = integer(0),
collabels = integer(0),
model = NULL,
nbcocluster,
strategy = coclusterStrategy(),
nbCore = 1
)
```

### Arguments

`data` |
Input data as matrix (or list containing data matrix, numeric vector for row effects and numeric vector column effects in case of contingency data with known row and column effects.) | |||||||||||||||||

`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` |
Integer Vector specifying the class of rows. The class number starts from zero. Provide -1 for unknown row class. | |||||||||||||||||

`collabels` |
Integer 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 Poisson 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. |

### Value

Return an object of `BinaryOptions`

or `ContingencyOptions`

or `ContinuousOptions`

depending on whether the data-type is Binary, Contingency or Continuous
respectively.

### Examples

```
## Simple example with simulated contingency data
## load data
data(contingencydataunknown)
## usage of coclusterContingency function in its most simplest form
strategy = coclusterStrategy( nbinititerations = 5, nbxem = 2, nbiterations_int = 2
, nbiterationsxem = 10, nbiterationsXEM = 100, epsilonXEM=1e-5)
out<-coclusterContingency( contingencydataunknown, nbcocluster=c(2,3), strategy = strategy)
## Summarize the output results
summary(out)
## Plot the original and Co-clustered data
plot(out)
```

*blockcluster*version 4.5.5 Index]