# 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. ```r 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. ## Examples ```r # 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) ``` ## See Also `handleMissings`

adjust function