The guided anomaly tour is a variation of the guided tour that is using an ellipse to determine anomalies on which to select target planes.
guided_anomaly_tour(
index_f,
d = 2,
alpha = 0.5,
cooling = 0.99,
max.tries = 25,
max.i = Inf,
ellipse,
ellc = NULL,
ellmu = NULL,
search_f = search_geodesic,
...
)Arguments
- index_f
the section pursuit index function to optimise. The function needs to take two arguments, the projected data, indexes of anomalies.
- d
target dimensionality
- alpha
the initial size of the search window, in radians
- cooling
the amount the size of the search window should be adjusted by after each step
- max.tries
the maximum number of unsuccessful attempts to find a better projection before giving up
- max.i
the maximum index value, stop search if a larger value is found
- ellipse
pxp variance-covariance matrix defining ellipse, default NULL. Useful for comparing data with some hypothesized null.
- ellc
This can be considered the equivalent of a critical value, used to scale the ellipse larger or smaller to capture more or fewer anomalies. Default 3.
- ellmu
This is the centre of the ellipse corresponding to the mean of the normal population. Default vector of 0's
- search_f
the search strategy to use
- ...
arguments sent to the search_f
Details
Usually, you will not call this function directly, but will pass it to
a method that works with tour paths like animate_xy,
save_history or render.
See also
slice_index for an example of an index functions.
search_geodesic, search_better,
search_better_random for different search strategies
Examples
animate_xy(flea[, 1:6], guided_anomaly_tour(anomaly_index(),
ellipse=cov(flea[,1:6])), ellipse=cov(flea[,1:6]), axes="off")
#> Converting input data to the required matrix format.
#> Using half_range 4.4
#> Target: 7.150, 6.0% better
#> Using half_range 4.4
#> Using ellc = 13
