Description Usage Arguments Value Author(s) References See Also Examples
This function uses a filter function f: X -> R^2 on a data set X that has n rows (observations) and k columns (variables).
1 2 3 4 |
distance_matrix |
an n x n matrix of pairwise dissimilarities |
filter_values |
a list of two length n vector of real numbers |
num_intervals |
a vector of two positive integers |
percent_overlap |
a number between 0 and 100 specifying how much adjacent intervals should overlap |
num_bins_when_clustering |
a positive integer that controls whether points in the same level set end up in the same cluster |
An object of class TDAmapper
which is a list of items named adjacency
(adjacency matrix for the edges), num_vertices
(integer number of vertices), level_of_vertex
(vector with level_of_vertex[i]
= index of the level set for vertex i), points_in_vertex
(list with points_in_vertex[[i]]
= vector of indices of points in vertex i), points_in_level
(list with points_in_level[[i]]
= vector of indices of points in level set i, and vertices_in_level
(list with vertices_in_level[[i]]
= vector of indices of vertices in level set i.
Paul Pearson, pearsonp@hope.edu
https://github.com/paultpearson/TDAmapper
1 2 3 4 5 6 7 8 9 10 11 12 | m2 <- mapper2D(
distance_matrix = dist(data.frame( x=2*cos(1:100), y=sin(1:100) )),
filter_values = list( 2*cos(1:100), sin(1:100) ),
num_intervals = c(5,5),
percent_overlap = 50,
num_bins_when_clustering = 10)
## Not run:
library(igraph)
g2 <- graph.adjacency(m2$adjacency, mode="undirected")
plot(g2, layout = layout.auto(g2) )
## End(Not run)
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