View source: R/find_outmost_partitioned_convexhull_points.R

find_outmost_partitioned_convexhull_points | R Documentation |

Function which finds the outermost convex hull points after making
`np`

samples and finding convex hull for each of them.
To be used as initial solution in archetypal analysis

```
find_outmost_partitioned_convexhull_points(df, kappas, np = 10,
nworkers = NULL)
```

`df` |
The data frame with dimensions n x d |

`kappas` |
The number of archetypes |

`np` |
The number of partitions that will be used (or the number of samples) |

`nworkers` |
The number of logical processors that will be used |

A list with members:

outmost, the first kappas most frequent outermost points as rows of data frame

outmostall, all the outermost points that have been found as rows of data frame

outmostfrequency, a matrix with frequency and cumulative frequency for outermost rows

`find_furthestsum_points`

, `find_outmost_projected_convexhull_points`

,

`find_outmost_convexhull_points`

& `find_outmost_points`

```
data("wd2") #2D demo
df = wd2
yy = find_outmost_partitioned_convexhull_points(df, kappas = 3, nworkers = 2)
yy$outmost #the rows of 3 outermost points
df[yy$outmost,] #the 3 outermost points
yy$outmostall #all outermost rows
yy$outmostfrequency #their frequency
```

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