# Select the Most Informative Components

### Description

Takes an array of observations as an input and outputs a subset of the components having the most extreme kurtoses.

### Usage

1 | ```
selectComponents(x, first = 2, last = 2)
``` |

### Arguments

`x` |
Numeric array of an order at least two. It is assumed that the last dimension corresponds to the sampling units. |

`first` |
Number of components with maximal kurtosis to be selected. Can equal zero but the total number of components selected must be at least two. |

`last` |
Number of components with minimal kurtosis to be selected. Can equal zero but the total number of components selected must be at least two. |

### Details

In independent component analysis (ICA) the components having the most extreme kurtoses are often thought to be also the most informative. With this viewpoint in mind the function `selectComponents`

selects from `x`

`first`

components having the highest kurtosis and `last`

components having the lowest kurtoses and outputs them as a standard data matrix for further analysis.

### Value

Data matrix with rows corresponding to the observations and the columns correponding to the `first`

+ `last`

selected components in decreasing order with respect to kurtosis. The names of the components in the output matrix correspond to the indices of the components in the original array `x`

.

### Author(s)

Joni Virta

### Examples

1 2 3 4 5 6 7 8 9 10 11 12 |