VARglm: Fit lag-1 VAR network using glm

In SachaEpskamp/multivar: Fit awesome psychological networks

Usage

VARglm(x, family, vars, adjacency, icfun = BIC, ...)

Arguments

x
family
vars
adjacency
icfun
...

Examples

##---- Should be DIRECTLY executable !! ----
##-- ==>  Define data, use random,
##--	or do  help(data=index)  for the standard data sets.

## The function is currently defined as
function (x, family, vars, adjacency, icfun = BIC, ...) 
{
    if (missing(x)) 
        stop("'x' must be assigned")
    x <- as.matrix(x)
    Ni <- ncol(x)
    Nt <- nrow(x)
    if (missing(vars)) 
        vars <- 1:Ni
    No <- length(vars)
    if (missing(adjacency)) 
        adjacency <- matrix(1, Ni, No)
    if (is.vector(adjacency)) 
        adjacency <- as.matrix(adjacency)
    if (!is.matrix(adjacency) && ncol(adjacency) != No && nrow(adjacency) != 
        Ni) 
        stop("'adjacency' must be square matrix with a row for each predictor and column for each outcome variable.")
    if (missing(family)) {
        if (identical(c(0, 1), sort(unique(c(x))))) 
            family <- rep("binomial", No)
        else family <- rep("gaussian", No)
    }
    if (length(family) == 1) {
        family <- list(family)
        if (No > 1) 
            for (i in 2:No) family[[i]] <- family[[1]]
    }
    if (length(family) != No) 
        stop("Length of family is not equal to number of outcome variables.")
    Out <- list()
    Out$graph <- matrix(0, Ni, No)
    Out$IC <- 0
    for (i in 1:No) {
        if (is.function(family[[i]])) 
            fam <- family[[i]]
        else fam <- get(family[[i]])
        if (any(as.logical(adjacency[, i]))) {
            Res <- glm(x[-1, vars[i]] ~ x[-nrow(x), as.logical(adjacency[, 
                i])], family = fam)
        }
        else {
            Res <- glm(x[-1, vars[i]] ~ NULL, family = fam)
        }
        Out$graph[as.logical(adjacency[, i]), i] <- coef(Res)[-1]
        Out$IC <- Out$IC + icfun(Res, ...)
    }
    return(Out)
  }

SachaEpskamp/multivar documentation built on May 9, 2019, 12:08 p.m.