Code used to compute lagged variables in predict.sarlm()

predict.sarlm.R

      K <- ifelse(colnames(X)[1] == "(Intercept)", 2, 1)
      m <- ncol(X)
      # check if there are enough regressors
      if (m > 1) {
        WX <- matrix(nrow=nrow(X),ncol=(m-(K-1)))
        for (k in K:m) {
          wx <- lag.listw(listw, X[,k], 
                          zero.policy=zero.policy)
          if (any(is.na(wx)))
            stop("NAs in lagged independent variable")
          WX[,(k-(K-1))] <- wx
        }
      }
      if (K == 2) {
        # unnormalized weight matrices
        if (!(listw$style == "W")) {
          intercept <- as.double(rep(1, nrow(X)))
          wx <- lag.listw(listw, intercept, 
                          zero.policy = zero.policy)
          if (m > 1) {
            WX <- cbind(wx, WX)
          } else {
            WX <- matrix(wx, nrow = nrow(X), ncol = 1)
          }
        } 
      }
      if (any(object$aliased)) {
        if (K>1 && (listw$style == "W")) colnames(WX) <- paste("lag.", colnames(X)[-1], sep="")
        else colnames(WX) <- paste("lag.", colnames(X), sep="")
        WX <- WX[,!colnames(WX) %in% names(object$aliased[object$aliased])]
      }
      if (legacy.mixed) Xo <- cbind(Xo, WX)