diff -r d59bea55db9b -r c445c931472f examples/xyacfs.mod
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/examples/xyacfs.mod	Mon Dec 06 13:09:21 2010 +0100
@@ -0,0 +1,56 @@
+/*Extended Yet Another Curve Fitting Solution (The poor man's RMA)
+
+  An extension of yacfs.mod adding a Weight parameter:
+    When set to 1 the model produces best fit by least squares with all error in y and none in x (YonX);
+    When set to zero the model produces best fit by least squares with all error in x and none in y (XonY);
+    When set to 0.5 the model assumes equal error in x and y producing results similar to fitting by Reduced Major Axis Analysis.
+
+  Nigel_Galloway@operamail.com
+  November 5th., 2009
+*/
+set Sample;
+param Sx {z in Sample};
+param Sy {z in Sample};
+param Weight := 0.5;
+
+var a;
+var b;
+var p;
+var q;
+
+XonY1 :sum{z in Sample} q*Sy[z]*Sy[z] + sum{z in Sample} p*Sy[z] = sum{z in Sample} Sy[z]*Sx[z];
+XonY2 :sum{z in Sample} q*Sy[z] + sum{z in Sample} p = sum{z in Sample} Sx[z];
+YonX1 :sum{z in Sample} a*Sx[z]*Sx[z] + sum{z in Sample} b*Sx[z] = sum{z in Sample} Sy[z]*Sx[z];
+YonX2 :sum{z in Sample} a*Sx[z] + sum{z in Sample} b = sum{z in Sample} Sy[z];
+
+solve;
+
+param W := Weight*a + (1-Weight)*(1/q);
+printf "\nbest linear fit is:\n\ty = %f %s %fx\n\n", b*Weight - (1-Weight)*(p/q), if W < 0 then "-" else "+", abs(W);
+
+data;
+
+param:
+Sample:   Sx    Sy :=
+  1         0    1
+  2       0.5  0.9
+  3         1  0.7
+  4       1.5  1.5
+  5       1.9    2
+  6       2.5  2.4
+  7         3  3.2
+  8       3.5    2
+  9         4  2.7
+ 10       4.5  3.5
+ 11         5    1
+ 12       5.5    4
+ 13         6  3.6
+ 14       6.6  2.7
+ 15         7  5.7
+ 16       7.6  4.6
+ 17       8.5    6
+ 18         9  6.8
+ 19        10  7.3
+;
+
+end;