TDAmapper (version 1.0)

mapper1D: mapper1D function

Description

This function uses a filter function f: X -> R on a data set X that has n rows (observations) and k columns (variables).

Usage

mapper1D(distance_matrix = dist(data.frame(x = 2 * cos(0.5 * (1:100)), y = sin(1:100))), filter_values = 2 * cos(0.5 * (1:100)), num_intervals = 10, percent_overlap = 50, num_bins_when_clustering = 10)

Arguments

distance_matrix

An n x n matrix of pairwise dissimilarities.

filter_values

A length n vector of real numbers.

num_intervals

A positive integer.

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.

Value

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.

References

https://github.com/paultpearson/TDAmapper

See Also

mapper2D

Examples

m1 <- mapper1D(
       distance_matrix = dist(data.frame( x=2*cos(0.5*(1:100)), y=sin(1:100) )),
       filter_values = 2*cos(0.5*(1:100)),
       num_intervals = 10,
       percent_overlap = 50,
       num_bins_when_clustering = 10)
## Not run: 
# #install.packages("igraph")
# library(igraph)
# g1 <- graph.adjacency(m1$adjacency, mode="undirected")
# plot(g1, layout = layout.auto(g1) )
# ## End(Not run)