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/*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;
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