Fixes #73.

Author: SebKrantz

R/EMBM_MQ.R

@@ -58,7 +58,7 @@ EMstepBMMQ = function(X, A, C, Q, R, Z_0, V_0, XW0, NW, dgind, dnkron, dnkron_in
     Qsr = (EZZ[sr, sr] - tcrossprod(A_new[sr, srp, drop = FALSE], EZZ_FB[sr, srp, drop = FALSE])) / TT
     Q_new[sr, sr] = if(rQi == 2L) Qsr else diag(diag(Qsr))
   } else Q_new[sr, sr] = diag(r)
-  Q_new[rpC1nq, rpC1nq] = diag(diag(crossprod(Zsmooth[, rpC1nq, drop = FALSE]) + sum3(Vsmooth[rpC1nq, rpC1nq,, drop = FALSE])) / TT)
+  Q_new[rpC1nq, rpC1nq] = diag(diag(crossprod(Zsmooth[, rpC1nq, drop = FALSE]) + sum3(Vsmooth[rpC1nq, rpC1nq,, drop = FALSE])) / TT, nrow = nq)
 
   # E(X'X) & E(X'Z)
   # Estimate matrix C using maximum likelihood approach

R/init_cond.R

@@ -129,7 +129,7 @@ init_cond_MQ <- function(X, X_imp, F_pc, v, n, r, p, TT, nq, rRi, rQi) {
   Q[1:r, 1:r] <- switch(rQi + 1L, diag(r), diag(fvar(var$res)), cov(var$res))
   Q[(rpC+1):(rpC+nq), (rpC+1):(rpC+nq)] <- if(rRi == 2L)
       cov(res[, -seq_len(nm), drop = FALSE], use = "pairwise.complete.obs") else if(rRi == 1L)
-      diag(fvar(res[, -seq_len(nm)], na.rm = TRUE)) else diag(nq)
+      diag(fvar(res[, -seq_len(nm), drop = FALSE], na.rm = TRUE), nrow = nq) else diag(nq)
   diag(Q)[diag(Q) == 0] <- 1e-6 # Prevent singularity in Kalman Filter