inputs_lmnet: Input preprocessing
In netregR: Regression of Network Responses

Description Usage Arguments Details Value See Also Examples

Prepare covariates and optional response in adjacency matrix form. If undirected, the values are drawn from the lower triangle of the adjacency matrices.

1 2	inputs_lmnet(Xlist, Y = NULL, directed = TRUE, add_intercept = TRUE, time_intercept = FALSE)

`Xlist`	List of n \times n \times tmax matrices, possibly containing response matrix labeled ‘Y’. Diagonals (self-loops) are ignored.
`Y`	Optional n \times n \times tmax response matrix. NAs in this matrix will be automatically removed. Diagonals (self-loops) are ignored.
`directed`	Optional logical indicator of whether input data is for a directed network, default is `TRUE`. Undirected data format is lower triangle of adjacencey matrix.
`add_intercept`	Optional logical indicator of whether intercept should be added to X, default is `TRUE`.
`time_intercept`	Optional logical indicator of whether separate intercept should be added to X for each observation of the relational matrix, default is `FALSE`.

This function takes a list of network covariates (in adjacency matrix form) and prepares them for the regression code lmnet. Accomodates 3-dimensional relational arrays with tmax repeated observations of the network (over time or context). Typical network data with a single observation may be input as matrices, i.e. tmax = 1.

A list of:

`Y`	Vector of responses (column-wise vectorization order) of appropriate length.
`X`	Matrix of covariates (column-wise vectorization order) of appropriate size.
`nodes`	2-column matrix (or 3-column for repeated observations) indicating directed relation pairs to which each entry in Y and each row in X corresponds.

lmnet, vhat_exch

# tmax = 1
set.seed(1)
n <- 10
Xlist <- list(matrix(rnorm(n^2),n,n), matrix(sample(c(0,1), n^2, replace=TRUE),n,n))
Xlist$Y <- matrix(rnorm(n^2), n, n)
Xlist$Y[1:5] <- NA
r <- inputs_lmnet(Xlist)
r
lmnet(r$Y,r$X,nodes=r$nodes)

# tmax = 4
set.seed(1)
n <- 10
tmax <- 4
X1 <- array(rnorm(n^2*tmax),c(n,n,tmax))
X2 <- array(sample(c(0,1), n^2*tmax, replace=TRUE), c(n,n,tmax))
Xlist <- list(X1, X2)
Xlist$Y <- array(rnorm(n^2)*tmax, c(n, n, tmax))
Xlist$Y[1:5] <- NA
r <- inputs_lmnet(Xlist)
head(r$nodes)