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dNetReorder is reorder the multiple graph colorings within a
sheet-shape rectangle grid
dNetReorder(
g,
data,
feature = c("node", "edge"),
node.normalise = c("none", "degree"),
xdim = NULL,
ydim = NULL,
amplifier = NULL,
metric = c("none", "pearson", "spearman", "kendall", "euclidean",
"manhattan", "cos",
"mi"),
init = c("linear", "uniform", "sample"),
algorithm = c("sequential", "batch"),
alphaType = c("invert", "linear", "power"),
neighKernel = c("gaussian", "bubble", "cutgaussian", "ep", "gamma")
)an object of class "igraph" or "graphNEL"
an input data matrix used to color-code vertices/nodes. One column corresponds to one graph node coloring. The input matrix must have row names, and these names should include all node names of input graph, i.e. V(g)$name, since there is a mapping operation. After mapping, the length of the patern vector should be the same as the number of nodes of input graph. The way of how to color-code is to map values in the pattern onto the whole colormap (see the next arguments: colormap, ncolors, zlim and colorbar)
the type of the features used. It can be one of either 'edge' for the edge feature or 'node' for the node feature. See 'Note' for explanations.
the normalisation of the nodes. It can be one of either 'none' for no normalisation or 'degree' for a node being penalised by its degree.
an integer specifying x-dimension of the grid
an integer specifying y-dimension of the grid
an integer specifying the amplifier (3 by default) of the number of component planes. The product of the component number and the amplifier constitutes the number of rectangles in the sheet grid
distance metric used to define the similarity between component planes. It can be "none", which means directly using column-wise vectors of codebook/data matrix. Otherwise, first calculate the covariance matrix from the codebook/data matrix. The distance metric used for calculating the covariance matrix between component planes can be: "pearson" for pearson correlation, "spearman" for spearman rank correlation, "kendall" for kendall tau rank correlation, "euclidean" for euclidean distance, "manhattan" for cityblock distance, "cos" for cosine similarity, "mi" for mutual information.
an initialisation method. It can be one of "uniform", "sample" and "linear" initialisation methods
the training algorithm. Currently, only "sequential" algorithm has been implemented
the alpha type. It can be one of "invert", "linear" and "power" alpha types
the training neighbor kernel. It can be one of "gaussian", "bubble", "cutgaussian", "ep" and "gamma" kernels
an object of class "sReorder", a list with following components:
nHex: the total number of rectanges in the grid
xdim: x-dimension of the grid
ydim: y-dimension of the grid
uOrder: the unique order/placement for each component
plane that is reordered to the "sheet"-shape grid with rectangular
lattice
coord: a matrix of nHex x 2, with each row corresponding
to the coordinates of each "uOrder" rectangle in the 2D map grid
call: the call that produced this result
# NOT RUN {
# 1) generate a random graph according to the ER model
g <- erdos.renyi.game(100, 1/100)
# 2) produce the induced subgraph only based on the nodes in query
subg <- dNetInduce(g, V(g), knn=0)
# 3) reorder the module with vertices being color-coded by input data
nnodes <- vcount(subg)
nsamples <- 10
data <- matrix(runif(nnodes*nsamples), nrow=nnodes, ncol=nsamples)
rownames(data) <- V(subg)$name
sReorder <- dNetReorder(g=subg, data, feature="node",
node.normalise="none")
# }
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