adjust: Adjust the dimensions of a source object to the dimensions of...

In btergm: Temporal Exponential Random Graph Models by Bootstrapped Pseudolikelihood

Adjust the dimensions of a source object to the dimensions of a target object

Description

Adjust the dimensions of a source object to the dimensions of a target object.

Usage

adjust(
  source,
  target,
  remove = TRUE,
  add = TRUE,
  value = NA,
  returnlabels = FALSE
)

Arguments

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 NA values, but other sensible values may include -Inf or 0.
returnlabels	Return a list of added and removed row and column labels rather than the actual matrix, vector, or network object?

Details

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.

See Also

handleMissings

Examples

# 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

## End(Not run)

btergm documentation built on May 29, 2024, 12:09 p.m.