Graphical interface to find outliers and/or to switch mislabeld landmarks
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
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 ## Not run: 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 ## End(Not run)
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