| Title: | Network Analysis and Visualization |
|---|---|
| Description: | Collection of functions for fast manipulation, handling, and analysis of large-scale networks based on family and social data. Functions are utility functions used to manipulate data in three "formats": sparse adjacency matrices, pedigree trio family data, and pedigree family data. When possible, the functions should be able to handle millions of data points quickly for use in combination with data from large public national registers and databases. Kenneth Lange (2003, ISBN:978-8181281135). |
| Authors: | Claus Thorn Ekstrøm [aut, cre], Bendix Carstensen [ctb] |
| Maintainer: | Claus Thorn Ekstrøm <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 0.1.3 |
| Built: | 2025-01-16 04:53:22 UTC |
| Source: | https://github.com/ekstroem/networkr |
Create an adjacency matrix from a set of nodes and edges.
adjacency(from, to, weight = 1, directed = TRUE)adjacency(from, to, weight = 1, directed = TRUE)
from |
a vector of nodes where the edges originate |
to |
a vector of nodes where the edges point to |
weight |
a numeric vector of weights |
directed |
logical. Are the edges directed (TRUE, the default) or bidirected(FALSE). |
Returns a sparse adjacency matrix
Claus Ekstrom [email protected]
from <- c("A", "A", "A", "B", "C") to <- c("B", "C", "D", "D", "E") adjacency(from, to) from <- c("A", "A", "A", "B", "C") to <- c("B", "C", "D", "D", "E") weights <- c(1, .5, 1, .7, 1) adjacency(from, to, weights)from <- c("A", "A", "A", "B", "C") to <- c("B", "C", "D", "D", "E") adjacency(from, to) from <- c("A", "A", "A", "B", "C") to <- c("B", "C", "D", "D", "E") weights <- c(1, .5, 1, .7, 1) adjacency(from, to, weights)
Hyperlink-induced topic search (HITS) is a link analysis algorithm that is also known as hubs and authorities. It rates nodes by comparing arrows pointing in and out of nodes in an asymmetrical graph.
hits(adjmatrix, maxiter = 100L, tol = 1e-05)hits(adjmatrix, maxiter = 100L, tol = 1e-05)
adjmatrix |
an adjacency matrix |
maxiter |
non-negative integer |
tol |
positive numeric value to be used as tolerance threshold for convergence |
Hubs are nodes with a lot of arrows pointing out while authorities are node with a lot of arrows pointing in.
Returns a list with three elements: authorities (a vector) of and hubs (a vector), and number of iterations used.
Claus Ekstrom [email protected]
Kleinberg, Jon (1999). "Authoritative sources in a hyperlinked environment" (PDF). Journal of the ACM. 46 (5): 604–632. doi:10.1145/324133.324140
from <- c("A", "A", "A", "B", "C") to <- c("B", "C", "D", "D", "E") hits(adjacency(from, to))from <- c("A", "A", "A", "B", "C") to <- c("B", "C", "D", "D", "E") hits(adjacency(from, to))
Create a vector of length n, giving the family id of each subject. If the pedigree is totally connected, then everyone will end up in tree 1, otherwise the tree numbers represent the disconnected subfamilies. Singleton subjects each have unique family numbers.
No check is done to ensure that the id, fid, and mid actually refer to proper family structure. References to ids in the fid and mid arguments that are not part of the id vector are considered founders and are thus replaced by NA or 0s after being used to group full and half-sibs.
make_family_id(id, fid, mid)make_family_id(id, fid, mid)
id |
Numeric vector of ids |
fid |
Numeric vector of ids of the father. This should be NA or 0 for a founder. |
mid |
Numeric vector of ids of the mother. This should be NA or 0 for a founder. |
Returns an integer vector giving the family index of each individual
Claus Ekstrom [email protected]
id <- 1:11 fid <- c(NA, NA, 1, 1, NA, 23, 45, 5, 5, 7, NA) mid <- c(NA, NA, 2, 2, 65, NA, 46, 6, 6, 6, 0) make_family_id(id, fid, mid)id <- 1:11 fid <- c(NA, NA, 1, 1, NA, 23, 45, 5, 5, 7, NA) mid <- c(NA, NA, 2, 2, 65, NA, 46, 6, 6, 6, 0) make_family_id(id, fid, mid)
Create a vector of length n, giving the id of parental chains. If the pedigree is totally connected, then everyone will end up in tree 1, otherwise the tree numbers represent the disconnected subfamilies. Singleton subjects each have unique family numbers.
No check is done to ensure that the id, fid, and mid actually refer to proper family structure. References to ids in the fid and mid arguments that are not part of the id vector are considered founders.
make_parental_chain(id, fid, mid)make_parental_chain(id, fid, mid)
id |
Numeric vector of ids |
fid |
Numeric vector of ids of the father. This should be NA or 0 for a founder. |
mid |
Numeric vector of ids of the mother. This should be NA or 0 for a founder. |
Returns an integer vector giving the family index
Claus Ekstrom [email protected]
id <- 1:11 fid <- c(0,0,1,0,0,4,0,0,3,7,7) mid <- c(0,0,2,0,0,5,0,0,6,6,8) make_parental_chain(id, fid, mid)id <- 1:11 fid <- c(0,0,1,0,0,4,0,0,3,7,7) mid <- c(0,0,2,0,0,5,0,0,6,6,8) make_parental_chain(id, fid, mid)
The function generates for each person lists of maternal half-sibs, paternal half-sibs and full sibs. Optionally these are expanded to separate columns in a data.table.
mksib(obj, ns = 3, expand.vars = TRUE)mksib(obj, ns = 3, expand.vars = TRUE)
obj |
A 3-column structure with column names |
ns |
Integer. The maximal no of sibs of each type to include in the result if sibling ids are required in separate columns. |
expand.vars |
Logical. Should the sibling ids be returned in separate
columns. If |
There are no checks of persons being both mother and father, nor being its
own parent and incest checks are not performed. In other words, the
obj is assumed to be sane, but possibly immoral.
A data.table with the columns of the obj and columns
for ns maternal, paternal and full sibs, named ms1,
ms2, ... ps1, ps2, ... fs1,
fs2.
If expand.vars=FALSE there will instead be three columns of lists
named msibs, psibs and fsibs.
Claus Thorn Ekstrøm, [email protected], Bendix Carstensen, [email protected]
library( data.table ) id <- 1:12 pid <- c(NA, 1, 1, 1, NA, 23, 45, 5, 5, 7, 12, NA) mid <- c(NA, NA, 2, 2, 12, NA, 46, 6, 6, 6, NA, 12) indd <- data.table( id, mid, pid ) indata <- copy( indd ) indata str( xx <- mksib( indata ) ) xx zz <- mksib( indata, 2, e=FALSE ) zzlibrary( data.table ) id <- 1:12 pid <- c(NA, 1, 1, 1, NA, 23, 45, 5, 5, 7, 12, NA) mid <- c(NA, NA, 2, 2, 12, NA, 46, 6, 6, 6, NA, 12) indd <- data.table( id, mid, pid ) indata <- copy( indd ) indata str( xx <- mksib( indata ) ) xx zz <- mksib( indata, 2, e=FALSE ) zz
Collection of miscellaneous useful and semi-useful functions and add-on functions that enhances a number of existing packages and provides In particular in relation to statistical genetics
| Package: | networkR |
| Type: | Package |
| Version: | 1.0 |
| Date: | 2012-03-29 |
| License: | GPL-2 |
how to use the package, including the most important ~~
Claus Thorn Ekstrøm [email protected]
Maintainer: Claus Thorn Ekstrøm
[email protected]
Ekstrøm, C. (2017). The R Primer. 2nd edition, Chapman & Hall.
Simple tests to chech the consistency of the pedigree trio family data. Currently the following checks are undertaken: 1) that no duplicates ids are found; 2) that the primary id is not missing for anyone; 3) that founders have both the father and mother id missing; 4) that individuals are not both classified as male (fathers and mothers);
validate_trio_consistency(id, fid, mid, sex = NULL)validate_trio_consistency(id, fid, mid, sex = NULL)
id |
Numeric. The id of the individual. These values should be unique |
fid |
Numeric. The father id. NA or 0 are used for missing. |
mid |
Numeric. The mother id. NA or 0 are used for missing. |
sex |
An optional numeric vector with the sex of the individual. Only four values should be present 1 (male), 2 (female), 0 or NA (missing) |
There are no checks of persons being both mother and father, nor being its
own parent and incest checks are not performed. In other words, the
obj is assumed to be sane, but possibly immoral.
Throws an error if an inconsistency is found. Otherwise returns TRUE.
Claus Thorn Ekstrøm, [email protected]
library("data.table") id <- 1:12 fid <- c(NA, 0, 1, 1, NA, 23, 45, 5, 5, 7, 10, 10) mid <- c(NA, NA, 2, 2, 0, 56, 46, 6, 6, 6, 9, 11) validate_trio_consistency(id, fid, mid)library("data.table") id <- 1:12 fid <- c(NA, 0, 1, 1, NA, 23, 45, 5, 5, 7, 10, 10) mid <- c(NA, NA, 2, 2, 0, 56, 46, 6, 6, 6, 9, 11) validate_trio_consistency(id, fid, mid)