Counts the number of times each link in a network occurs in each unique link position within the motifs
link_positions(M, six_node = FALSE, weights, normalisation = "none")A numeric matrix representing interactions between two groups of nodes. Each row corresponds to a node in one level and each column corresponds to a node in the other level. Elements of M are positive numbers if nodes interact, and 0 otherwise. Formally, M is a biadjacency matrix. When nodes i and j interact, m_ij > 0; if they do not interact, m_ij = 0.
Logical; should positions in six node motifs be counted? Defaults to FALSE.
Logical; Should weights of the links be taken into account?
Which normalisation should be used: 'none','sum', 'position', 'sizeclass', 'sizeclass_plus1', 'sizeclass_NAzero', 'levelsize', 'levelsize_plus1', 'levelsize_NAzero','motif', 'motif_plus1' or 'motif_NAzero'? Defaults to "none". (see details)
Returns a data frame with one column for each link position: 29 columns if six_node is FALSE, and 106 columns if six_node is TRUE.
Columns names are given as "lpx" where x is the ID of the position as described in the motif dictionary. To view the 'motif dictionary' showing
which link position a given ID corresponds to, enter vignette("bmotif-dictionary").
Each row corresponds to one link in the network. Row names are gives as "x -- y", where x is the species in the first level (rows) and y is the species in the second level (columns).
By default, the elements of the data frame will be the raw link position counts.
If weight = TRUE, link position counts will be multiplied by the link weight.
If normalisation is set to "sum", "sizeclass" or "position", the elements will be
normalised position counts as described above.
Counts the number of times each link in a network occurs in each of the 29 (if six_node = FALSE) or 106 (if six_node = TRUE) unique link positions within motifs (to quantify a link's structural role).
If six_node = FALSE, link positions in all motifs containing between 2 and 5 nodes are counted. If six_node = TRUE, link positions in all motifs containing between 2 and 6 nodes are counted. Analyses where six_node = FALSE are substantially faster
than when six_node = TRUE, especially for large networks. For large networks, counting six node motifs is also memory intensive. In some cases, R can crash if there is not enough memory.
If interactions are weighted (non-zero matrix elements take values other than 1), these can be incorporated by setting weights = TRUE.
If weights = TRUE, the function will return the number of times each link occurs in each position,
multiplied by the weight of the link, following Mora et al. (2018).
Links between nodes with more interactions will tend to appear in more positions. Normalisation helps control for this effect. bmotif include four types of normalisation:
"none": performs no normalisation and will return the raw position counts.
"sum": divides the position measure for each link by the total number of times that link appears in any position (divides each element in a row by the row sum).
"position": divides the position measure for each link by the total number of times any link occurs in that link position (divides each element in a column by the column sum). This gives a measure of how often a link occurs in a position relative to the other links in the network.
Size class normalisation
"sizeclass": divides the position measure for each link by the total number of times that link appears in any position within the same motif size class (the number of nodes a motif contains).
"sizeclass_plus1": same as 'sizeclass' but adds one to all position measure values. If a link does not occur in any motifs in a given size class, 'sizeclass' normalisation will return NAs. 'sizeclass_plus1' avoids this by adding one to all counts.
"sizeclass_NAzero": same as 'sizeclass' but replaces all NA values with 0. If a link does not occur in any motifs in a given size class, 'sizeclass' normalisation will return NAs. 'sizeclass_NAzero' avoids this by replacing NAs with zero.
Levelsize normalisation
"levelsize": divides the position measure for each link by the total number of times that link appears in any position within a motif with a given number of nodes in the top level and the bottom level. For example, the relative frequencies of all position measures in motifs with three nodes in the top level and two nodes in the bottom level will sum to one, as will the relative frequency of all position measures in motifs with 2 nodes in the top level and two nodes in the bottom level, and so on.
"levelsize_plus1": same as 'levelsize' but adds one to all position measure values. If a link does not occur in any motifs with a given number of nodes in the top level and the bottom level, 'levelsize' normalisation will return NAs. 'levelsize_plus1' avoids this by adding one to all counts.
"levelsize_NAzero": same as 'levelsize' but replaces all NA values with 0. If a link does not occur in any motifs with a given number of nodes in the top level and the bottom level, 'levelsize' normalisation will return NAs. 'levelsize_NAzero' avoids this by replacing NAs with zero.
Motif normalisation
"motif": divides the position measure for each link by the total number of times that link appears in any position within the same motif. For example, the relative frequencies of all position measures in motif 5 will sum to one, as will the relative frequency of all position measures in motif 10, and so on.
"motif_plus1": same as 'motif' but adds one to all position measure values. If a link does not occur in a particular motif, 'motif' normalisation will return NAs. 'motif_plus1' avoids this by adding one to all counts.
"motif_NAzero": same as 'motif' but replaces all NA values with 0. If a link does not occur in a particular motif, 'levelsize' normalisation will return NAs. 'motif_NAzero' avoids this by replacing NAs with zero.
If a matrix is provided without row or column names, default names will be assigned: the first row will be called called 'r1', the second row will be called 'r2' and so on. Similarly, the first column will be called 'c1', the second column will be called 'c2' and so on.
Mora, B.B., Cirtwill, A.R. and Stouffer, D.B., 2018. pymfinder: a tool for the motif analysis of binary and quantitative complex networks. bioRxiv, 364703.
Simmons, B. I., Sweering, M. J. M., Dicks, L. V., Sutherland, W. J. and Di Clemente, R. bmotif: a package for counting motifs in bipartite networks. bioRxiv. doi: 10.1101/302356
# NOT RUN {
set.seed(123)
row <- 10
col <- 10
# link positions in a binary network
m <- matrix(sample(0:1, row*col, replace=TRUE), row, col)
link_positions(M = m, six_node = TRUE, weights = FALSE, normalisation = "none")
# link positions in a weighted network
m[m>0] <- stats::runif(sum(m), 0, 100)
link_positions(M = m, six_node = TRUE, weights = TRUE, normalisation = "none")
# }
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