View source: R/find.outliers.r

find.outliers | R Documentation |

Graphical interface to find outliers and/or to switch mislabeld landmarks

```
find.outliers(
A,
color = 4,
lwd = 1,
lcol = 2,
mahalanobis = FALSE,
PCuse = NULL,
text = TRUE,
reflection = FALSE
)
```

`A` |
Input k x m x n real array, where k is the number of points, m is the number of dimensions, and n is the sample size. |

`color` |
color of Landmarks points to be plotted |

`lwd` |
linewidth visualizing distances of the individual landmarks from mean. |

`lcol` |
color of lines visualizing distances of the individual landmarks from mean. |

`mahalanobis` |
logical: use mahalanobis distance to find outliers. |

`PCuse` |
integer: Restrict mahalanobis distance to the first n Principal components. |

`text` |
logical: if |

`reflection` |
logical: specify whether reflections are allowed for superimpositioning. |

This function performs a procrustes fit and sorts all specimen according to their distances (either Procrustes or Mahalanobis-distance) to the sample's consensus. It provides visual help for rearranging landmarks and/or excluding outliers.

`data.cleaned` |
array (in original coordinate system) containing the changes applied and outliers eliminated |

`outlier` |
vector with integers indicating the positions in the original array that have been marked as outliers |

`dist.sort` |
table showing the distance to mean for each observation - decreasing by distance |

`type` |
what kind of distance was used |

Stefan Schlager

`typprob`

,`typprobClass`

```
data(boneData)
## look for outliers using the mahalanobis distance based on the first
# 10 PCscores
# to perform the example below, you need,of course, uncomment the answers
if (interactive()){
outliers <- find.outliers(boneLM, mahalanobis= TRUE, PCuse=10)
# n # everything is fine
# n # proceed to next
# s # let's switch some landmarks (3 and 4)
# 3
# 4
# n # we are done
# y # yes, because now it is an outlier
# s #enough for now
}
```

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