Adjust the dimensions of a source object to the dimensions of a target object
Adjust the dimensions of a source object to the dimensions of a target object.
adjust( source, target, remove = TRUE, add = TRUE, value = NA, returnlabels = FALSE )
source |
A matrix, network, list or data.frame object or a vector which should be adjusted. |
target |
A matrix, network, list or data.frame object or a vector to which the source object is compared with regard to its labels. |
remove |
Should rows and columns that are not present in the target object be removed? |
add |
Should rows and columns that are present in the target object but not in the source object be added to the source object? |
value |
The value to be inserted if a new row or column is added. By
default, new cells are filled with |
returnlabels |
Return a list of added and removed row and column labels rather than the actual matrix, vector, or network object? |
An adjacency matrix (the source matrix) is compared to another
adjacency matrix (the target matrix) by matching the row or column
labels. If the target matrix contains rows/columns which are not present in
the source matrix, new rows and columns with the corresponding labels and
NA values in the cells are inserted into the source matrix. If the
source matrix contains rows/columns which are not present in the target
matrix, these rows and columns are removed from the source matrix. In
addition to adjacency matrices, two-mode matrices, network objects (also with
vertex attributes), and vectors are supported.
Note that it is not necessary to use this function to preprocess any data before estimating a TERGM. The estimation functions in the btergm package call this function repeatedly to mutually adjust all data as needed.
# create sociomatrix a with 13 vertices a to m
vertices <- letters[1:13]
a <- matrix(rbinom(length(vertices)^2, 1, 0.1), nrow = length(vertices))
rownames(a) <- colnames(a) <- vertices
# create matrix b with the same vertices except f and k, but additional n
vertices <- c(vertices[-c(6, 11)], "n")
b <- matrix(rbinom(length(vertices)^2, 1, 0.1), nrow = length(vertices))
rownames(b) <- colnames(b) <- vertices
# check dimensions
dim(a) # 13 x 13
dim(b) # 12 x 12
# adjust a to b: add n and fill up with NAs; remove f and k
adjust(a, b, add = TRUE, remove = TRUE)
## Not run:
# more complex example with additional attributes stored in the network
# object; convert a to network object with additional vertex and network
# attributes
nw <- network(a)
vertices <- letters[1:13]
nwattrib1 <- matrix(rbinom(length(vertices)^2, 1, 0.1),
nrow = length(vertices))
nwattrib2 <- nwattrib1
rownames(nwattrib1) <- colnames(nwattrib1) <- vertices
set.network.attribute(nw, "nwattrib1", nwattrib1)
set.network.attribute(nw, "nwattrib2", nwattrib2)
set.vertex.attribute(nw, "vattrib", 1:length(vertices))
# check presence of the two attributes
list.network.attributes(nw) # nwattrib1 and nwattrib2 are listed
get.network.attribute(nw, "nwattrib1") # returns sociomatrix with labels
get.network.attribute(nw, "nwattrib2") # returns sociomatrix without labels
list.vertex.attributes(nw) # vattrib is listed
get.vertex.attribute(nw, "vattrib") # returns numeric vector 1:13
# adjust the network including the two attributes
nw.adjusted <- adjust(nw, b, add = TRUE, remove = TRUE)
as.matrix(nw.adjusted) # note that the order of nodes may have changed
get.network.attribute(nw.adjusted, "nwattrib1") # returns adjusted matrix
get.network.attribute(nw.adjusted, "nwattrib2") # returns adjusted matrix
get.vertex.attribute(nw.adjusted, "vattrib") # returns adjusted vector
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