% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/dot-calcProps.R
\name{.calcProps}
\alias{.calcProps}
\title{Calculate network properties}
\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{
\item{adjaMat}{adjacency matrix}

\item{dissMat}{dissimilarity matrix}

\item{assoMat}{association matrix}

\item{avDissIgnoreInf}{logical indicating whether to ignore infinities when 
calculating the average dissimilarity. If \code{FALSE} (default), infinity 
values are set to 1.}

\item{sPathNorm}{logical. If \code{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 \code{FALSE}, the shortest path is the minimum sum of dissimilarities 
between two nodes.}

\item{sPathAlgo}{character indicating the algorithm used for computing
the shortest paths between all node pairs. \code{\link[igraph]{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.}

\item{normNatConnect}{logical. If \code{TRUE} (default), the normalized 
natural connectivity is returned.}

\item{weighted}{logical indicating whether the network is weighted.}

\item{isempty}{logical indicating whether the network is empty.}

\item{clustMethod}{character indicating the clustering algorithm. Possible
values are \code{"hierarchical"} for a hierarchical algorithm based on
dissimilarity values, or the clustering methods provided by the igraph
package (see \code{\link[igraph]{communities}} for possible methods).
Defaults to \code{"cluster_fast_greedy"} for association-based networks and
to \code{"hierarchical"} for sample similarity networks.}

\item{clustPar}{list with parameters passed to the clustering functions.
If hierarchical clustering is used, the parameters are passed to
\code{\link[stats]{hclust}} as well as \code{\link[stats]{cutree}}.}

\item{weightClustCoef}{logical indicating whether (global) clustering 
coefficient should be weighted (\code{TRUE}, default) or unweighted 
(\code{FALSE}).}

\item{hubPar}{character vector with one or more elements (centrality 
measures) used for identifying hub nodes. Possible values are \code{degree},
\code{betweenness}, \code{closeness}, and \code{eigenvector}. If multiple
measures are given, hubs are nodes with highest centrality for all selected
measures. See details.}

\item{hubQuant}{quantile used for determining hub nodes. Defaults to 0.95.}

\item{lnormFit}{hubs are nodes with a centrality value above the 95\%
quantile of the fitted log-normal distribution (if \code{lnormFit = TRUE})
or of the empirical distribution of centrality values 
(\code{lnormFit = FALSE}; default).}

\item{connectivity}{logical. If \code{TRUE} (default), edge and vertex 
connectivity are calculated. Might be disabled to reduce execution time.}

\item{graphlet}{logical. If \code{TRUE} (default), graphlet-based network 
properties are computed: orbit counts of graphlets with 2-4 nodes 
(\code{ocount}) and Graphlet Correlation Matrix (\code{gcm}).}

\item{orbits}{numeric vector with integers from 0 to 14 defining the graphlet 
orbits.}

\item{weightDeg}{logical. If \code{TRUE}, the weighted degree is used (see
\code{\link[igraph]{strength}}). Default is \code{FALSE}. 
Is automatically set to \code{TRUE} for a fully connected (dense) network.}

\item{normDeg, normBetw, normClose, normEigen}{logical. If \code{TRUE} 
(default for all measures), a normalized version of the respective 
centrality values is returned.}

\item{centrLCC}{logical indicating whether to compute centralities only 
for the largest connected component (LCC). If \code{TRUE} 
(default), centrality values of disconnected components are zero.}

\item{jaccard}{shall the Jaccard index be calculated?}

\item{jaccQuant}{quantile for the Jaccard index}

\item{verbose}{integer indicating the level of verbosity. Possible values:
\code{"0"}: no messages, \code{"1"}: only important messages,
\code{"2"}(default): all progress messages are shown. Can also be logical.}
}
\description{
Calculates network properties for a given adjacency matrix
}
