diff --git a/R/make_dsem_ram.R b/R/make_dsem_ram.R
index 0df2bf0..e926e1e 100644
--- a/R/make_dsem_ram.R
+++ b/R/make_dsem_ram.R
@@ -332,7 +332,7 @@ function( sem,
   }
 
   # Loop through paths
-  P_kk = drop0(sparseMatrix( i=1, j=1, x=0, dims=rep(length(variables)*length(times),2) ))   # Make with a zero
+  G_kk = P_kk = drop0(sparseMatrix( i=1, j=1, x=0, dims=rep(length(variables)*length(times),2) ))   # Make with a zero
   #P_kk = new("dgCMatrix")
   #P_kk = Matrix()
   #P_kk@Dim <- as.integer(rep(length(variables)*length(times),2))
@@ -350,9 +350,9 @@ function( sem,
                          dims = rep(length(variables),2) )
     tmp_kk = kronecker(P_jj, L_tt)
     if(abs(as.numeric(model[i,'direction']))==1){
-      P_kk = P_kk + tmp_kk * par.nos[i]
+      P_kk = P_kk + tmp_kk * i
     }else{
-      G_kk = G_kk + tmp_kk * par.nos[i]
+      G_kk = G_kk + tmp_kk * i
     }
     #for( t in seq_along(times) ){
     #  # Get index for "from"
@@ -372,7 +372,9 @@ function( sem,
   f = \(x) matrix(unlist(mat2triplet(x)),ncol=3)
   ram = rbind( cbind(1, f(P_kk)),
                cbind(2, f(G_kk)) )
-  ram = data.frame( ram, startvalues[ram[,4]] )
+  ram = data.frame( ram[,1:3,drop=FALSE],
+                    as.numeric(par.nos)[ram[,4]],
+                    as.numeric(startvalues)[ram[,4]] )
   colnames(ram) = c("heads", "to", "from", "parameter", "start")
 
   #
diff --git a/vignettes/dynamic_factor_analysis.Rmd b/vignettes/dynamic_factor_analysis.Rmd
index 737822f..51c4897 100644
--- a/vignettes/dynamic_factor_analysis.Rmd
+++ b/vignettes/dynamic_factor_analysis.Rmd
@@ -154,33 +154,33 @@ tsdata = ts( cbind(tsdata, newcols), start=1978)
 tsdata = scale( tsdata, center=TRUE, scale=TRUE )
 
 # Automated version
-sem = make_dfa( variables = c("SJI","EBays","SJF","PSnd","HC"),
-                n_factors = n_factors )
+#sem = make_dfa( variables = c("SJI","EBays","SJF","PSnd","HC"),
+#                n_factors = n_factors )
 # Manual specification to show structure, using equations-and-lags interface
-#equations = "
-#  # Loadings of variables onto factors
-#  SJI = L11(0.1) * F1
-#  EBays = L12(0.1) * F1 + L22(0.1) * F2
-#  SJF = L13(0.1) * F1 + L23(0.1) * F2
-#  PSnd = L14(0.1) * F1 + L24(0.1) * F2
-#  HC = L15(0.1) * F1 + L25(0.1) * F2
-#
-#  # random walk for factors
-#  F1 = NA(1) * lag[F1,1]
-#  F2 = NA(1) * lag[F2,1]
-#
-#  # Unit variance for factors
-#  V(F1) = NA(1)
-#  V(F2) = NA(1)
-#
-#  # Zero residual variance for variables
-#  V(SJI) = NA(0)
-#  V(EBays) = NA(0)
-#  V(SJF) = NA(0)
-#  V(PSnd) = NA(0)
-#  V(HC) = NA(0)
-#"
-#sem = convert_equations(equations)
+equations = "
+  # Loadings of variables onto factors
+  SJI = L11(0.1) * F1
+  EBays = L12(0.1) * F1 + L22(0.1) * F2
+  SJF = L13(0.1) * F1 + L23(0.1) * F2
+  PSnd = L14(0.1) * F1 + L24(0.1) * F2
+  HC = L15(0.1) * F1 + L25(0.1) * F2
+
+  # random walk for factors
+  F1 = NA(1) * lag[F1,1]
+  F2 = NA(1) * lag[F2,1]
+
+  # Unit variance for factors
+  V(F1) = NA(1)
+  V(F2) = NA(1)
+
+  # Zero residual variance for variables
+  V(SJI) = NA(0)
+  V(EBays) = NA(0)
+  V(SJF) = NA(0)
+  V(PSnd) = NA(0)
+  V(HC) = NA(0)
+"
+sem = convert_equations(equations)
 
 # Initial fit
 mydsem0 = dsem( tsdata = tsdata,