FindRationalStarts {CICA} | R Documentation |

## Plot method for rstarts object

### Description

Two step clustering for finding rational start partitions

### Usage

```
FindRationalStarts(
DataList,
RatStarts = "all",
nComp,
nClus,
scalevalue = NULL,
center = TRUE,
verbose = TRUE,
pseudo = NULL,
pseudoFac = NULL
)
## S3 method for class 'rstarts'
plot(x, type = 1, mdsdim = 2, nClus = NULL, ...)
```

### Arguments

`DataList` |
a list of matrices |

`RatStarts` |
type of rational start. 'all' computes all types of hclust methods |

`nComp` |
number of ICA components to extract |

`nClus` |
Number of clusters for rectangles in dendrogram, default NULL is based on number of clusters present in the object |

`scalevalue` |
scale each matrix to have an equal sum of squares |

`center` |
mean center matrices |

`verbose` |
print output to console |

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

`pseudoFac` |
how many pseudo starts per rational start |

`x` |
an object of |

`type` |
type of plot, 1 for a dendrogram, 2 for a multidimensional scaling configuration |

`mdsdim` |
2 for two dimensional mds configuration, 3 for a three dimensional configuration |

`...` |
optional arguments passed to |

### Value

dataframe with (pseudo-) rational and dist object based on the pairwise modified RV values

### References

Durieux, J., & Wilderjans, T. F. (2019). Partitioning subjects based on high-dimensional fMRI data: comparison of several clustering methods and studying the influence of ICA data reduction in big data. Behaviormetrika, 46(2), 271-311.

### Examples

```
## Not run:
CICA_data <- Sim_CICA(Nr = 15, Q = 5, R = 4, voxels = 100, timepoints = 10,
E = 0.4, overlap = .25, externalscore = TRUE)
rats <- FindRationalStarts(DataList = CICA_data$X, nComp = 5, nClus = 4,verbose = TRUE, pseudo = .2)
plot(rats, type = 1, method = 'ward.D2')
plot(rats, type = 2, method = 'ward.D2')
plot(rats, type = 2, method = 'ward.D2', mdsdim = 3)
## End(Not run)
## Not run:
CICA_data <- Sim_CICA(Nr = 15, Q = 5, R = 4, voxels = 100, timepoints = 10,
E = 0.4, overlap = .25, externalscore = TRUE)
Out_starts <- FindRationalStarts(DataList = CICA_data$X,nComp = 5,nClus = 4,scalevalue = 1000)
plot(Out_starts)
plot(Out_starts, type = 2)
plot(Out_starts, type = 2,mdsdim = 3, method = 'ward.D2')
## End(Not run)
```

*CICA*version 1.1.1 Index]