dot-calcProps: Calculate network properties
In stefpeschel/NetCoMi: Network Construction and Comparison for Microbiome Data

.calcProps

R Documentation

Calculate network properties

Description

Calculates network properties for a given adjacency matrix

Usage

.calcProps(
  adjaMat,
  dissMat,
  assoMat,
  avDissIgnoreInf,
  sPathNorm,
  sPathAlgo,
  normNatConnect,
  weighted,
  isempty,
  clustMethod,
  clustPar,
  weightClustCoef,
  hubPar,
  hubQuant,
  lnormFit,
  connectivity,
  graphlet,
  orbits,
  weightDeg,
  normDeg,
  normBetw,
  normClose,
  normEigen,
  centrLCC,
  jaccard = FALSE,
  jaccQuant = NULL,
  verbose = 0
)

Arguments

`adjaMat`	adjacency matrix
`dissMat`	dissimilarity matrix
`assoMat`	association matrix
`avDissIgnoreInf`	logical indicating whether to ignore infinities when calculating the average dissimilarity. If `FALSE` (default), infinity values are set to 1.
`sPathNorm`	logical. If `TRUE` (default), shortest paths are normalized by average dissimilarity (only connected nodes are considered), i.e., a path is interpreted as steps with average dissimilarity. If `FALSE`, the shortest path is the minimum sum of dissimilarities between two nodes.
`sPathAlgo`	character indicating the algorithm used for computing the shortest paths between all node pairs. `distances` (igraph) is used for shortest path calculation. Possible values are: "unweighted", "dijkstra" (default), "bellman-ford", "johnson", or "automatic" (the fastest suitable algorithm is used). The shortest paths are needed for the average (shortest) path length and closeness centrality.
`normNatConnect`	logical. If `TRUE` (default), the normalized natural connectivity is returned.
`weighted`	logical indicating whether the network is weighted.
`isempty`	logical indicating whether the network is empty.
`clustMethod`	character indicating the clustering algorithm. Possible values are `"hierarchical"` for a hierarchical algorithm based on dissimilarity values, or the clustering methods provided by the igraph package (see `communities` for possible methods). Defaults to `"cluster_fast_greedy"` for association-based networks and to `"hierarchical"` for sample similarity networks.
`clustPar`	list with parameters passed to the clustering functions. If hierarchical clustering is used, the parameters are passed to `hclust` as well as `cutree`.
`weightClustCoef`	logical indicating whether (global) clustering coefficient should be weighted (`TRUE`, default) or unweighted (`FALSE`).
`hubPar`	character vector with one or more elements (centrality measures) used for identifying hub nodes. Possible values are `degree`, `betweenness`, `closeness`, and `eigenvector`. If multiple measures are given, hubs are nodes with highest centrality for all selected measures. See details.
`hubQuant`	quantile used for determining hub nodes. Defaults to 0.95.
`lnormFit`	hubs are nodes with a centrality value above the 95% quantile of the fitted log-normal distribution (if `lnormFit = TRUE`) or of the empirical distribution of centrality values (`lnormFit = FALSE`; default).
`connectivity`	logical. If `TRUE` (default), edge and vertex connectivity are calculated. Might be disabled to reduce execution time.
`graphlet`	logical. If `TRUE` (default), graphlet-based network properties are computed: orbit counts of graphlets with 2-4 nodes (`ocount`) and Graphlet Correlation Matrix (`gcm`).
`orbits`	numeric vector with integers from 0 to 14 defining the graphlet orbits.
`weightDeg`	logical. If `TRUE`, the weighted degree is used (see `strength`). Default is `FALSE`. Is automatically set to `TRUE` for a fully connected (dense) network.
`normDeg, normBetw, normClose, normEigen`	logical. If `TRUE` (default for all measures), a normalized version of the respective centrality values is returned.
`centrLCC`	logical indicating whether to compute centralities only for the largest connected component (LCC). If `TRUE` (default), centrality values of disconnected components are zero.
`jaccard`	shall the Jaccard index be calculated?
`jaccQuant`	quantile for the Jaccard index
`verbose`	integer indicating the level of verbosity. Possible values: `"0"`: no messages, `"1"`: only important messages, `"2"`(default): all progress messages are shown. Can also be logical.