pcvr (version 1.2.0)

pcv.net: Network analysis of a distance matrix

Description

Easy igraph use with pcv.emd output

Usage

pcv.net(
  emd = NULL,
  meta = NULL,
  dissim = TRUE,
  distCol = "emd",
  filter = 0.5,
  direction = "greater"
)

Value

Returns a list containing three elements: nodes: A dataframe of node data. edges: A dataframe of edges between nodes. graph: The network as an igraph object

Arguments

emd

A long dataframe as returned by pcv.emd. Currently this function is only made to work with dataframe output, not distance matrix output.

meta

Metadata to be carried from pcv.emd output into the network, defaults to NULL which will use all metadata. Type conversion will be attempted for these columns.

dissim

Logical, should the distCol be inverted to make a dissimilarity value?

distCol

The name of the column containing distances/dissimilarities. Defaults to "emd" for compatability with pcv.emd

filter

This can be either a numeric (0.5) in which case it is taken as a filter where only edges with values greater than or equal to that number are kept or a character string ("0.5") in which case the strongest X percentage of edges are kept. This defaults to 0.5 which does some filtering, although that should not be considered the best behavior for every setting. If this is NULL then your network will be almost always be a single blob, if set too high there will be very few nodes. Note that this filtering happens after converting to dissimilarity if dissim=TRUE.

direction

Direction of filtering, can be either "greater" or "lesser".

Examples

library(extraDistr)
dists <- list(
  rmixnorm = list(mean = c(70, 150), sd = c(15, 5), alpha = c(0.3, 0.7)),
  rnorm = list(mean = 90, sd = 3)
)
x <- mvSim(
  dists = dists, n_samples = 5, counts = 1000,
  min_bin = 1, max_bin = 180, wide = TRUE
)
emd_df <- pcv.emd(x,
  cols = "sim", reorder = c("group"), mat = FALSE,
  plot = FALSE, parallel = 1
)
net <- pcv.net(emd_df, meta = "group")
net2 <- pcv.net(emd_df, meta = "group", filter = "0.9", direction = "lesser")