CICA {CICA} | R Documentation |

## CICA: Clusterwise Independent Component Analysis

### Description

Main function to perform Clusterwise Independent Component Analysis

### Usage

```
CICA(
DataList,
nComp,
nClus,
method = "fastICA",
RanStarts,
RatStarts = NULL,
pseudo = NULL,
pseudoFac,
userDef = NULL,
userGrid = NULL,
scalevalue = 1000,
center = TRUE,
maxiter = 100,
verbose = TRUE,
ctol = 1e-06,
checks = TRUE
)
```

### Arguments

`DataList` |
a list of matrices |

`nComp` |
number or vector of ICA components per cluster |

`nClus` |
number or vector of clusters |

`method` |
Component method, default is |

`RanStarts` |
number of random starts |

`RatStarts` |
Generate rational starts. Either 'all' or a specific linkage method name (e.g., 'complete'). Use NULL to indicate that Rational starts should not be used. |

`pseudo` |
percentage value for perturbating rational starts to obtain pseudo rational starts |

`pseudoFac` |
factor to multiply the number of rational starts (7 in total) to obtain pseudorational starts |

`userDef` |
a user-defined starting seed stored in a data.frame, if NULL no userDef starting partition is used |

`userGrid` |
user supplied data.frame for multiple model CICA. First column are the requested components. Second column are the requested clusters |

`scalevalue` |
desired sum of squares of the block scaling procedure |

`center` |
mean center matrices |

`maxiter` |
maximum number of iterations for each start |

`verbose` |
print loss information to console |

`ctol` |
tolerance value for convergence criterion |

`checks` |
boolean parameter that indicates whether the input checks should be run (TRUE) or not (FALSE). |

### Value

`CICA`

returns an object of `class`

"CICA". It contains the estimated clustering, cluster specific component matrices and subject specific time course matrices

`P` |
partitioning vector of size |

`Sr` |
list of size |

`Ais` |
list of size |

`Loss` |
loss function value of the best start |

`FinalLossDiff` |
value of the loss difference between the last two iterations of the algorithm. |

`IndLoss` |
a vector with containing the individual loss function values |

`LossStarts` |
loss function values of all starts |

`Iterations` |
Number of iterations |

`starts` |
dataframe with the used starting partitions |

### Author(s)

Jeffrey Durieux

### Examples

```
## Not run:
CICA_data <- Sim_CICA(Nr = 15, Q = 5, R = 4, voxels = 100, timepoints = 10,
E = 0.4, overlap = .25, externalscore = TRUE)
multiple_output = CICA(DataList = CICA_data$X, nComp = 2:6, nClus = 1:5,
method = 'fastICA',userGrid = NULL, RanStarts = 30, RatStarts = NULL,
pseudo = c(0.1, 0.2),pseudoFac = 2, userDef = NULL, scalevalue = 1000,
center = TRUE,maxiter = 100, verbose = TRUE, ctol = .000001)
summary(multiple_output$Q_5_R_4)
plot(multiple_output$Q_5_R_4)
## End(Not run)
```

*CICA*version 1.1.1 Index]