Comparision with output from v.transform

First the output from R:
Looking at the residuals from the regression model used to map bad coordinates (x,y) to good coordinates (nx,ny):

## we made the linear model object 'l' above
## extract as dataframe
## residuals are computed for x and y separately
r <- as.data.frame(resid(l))

&nbsp;
## compute vector product of the (x,y) residuals:
c$resid <- sqrt(r$nx^2 + r$ny^2)
print(c)
          x          y       nx         ny      resid
1  -2078417 -14810.570 -2078314 -14838.378  46.617600
2  -2078743 -16057.955 -2078636 -16081.790  62.041274
3  -2077261 -16435.348 -2077170 -16463.156  40.905132
4  -2076709 -14405.369 -2076606 -14433.177  29.406399
5  -2074179 -15830.248 -2074084 -15901.558  33.111981
6  -2073850 -15707.435 -2073763 -15798.554  37.362736
7  -2073450 -13873.171 -2073359 -13920.712  21.623235
8  -2072359 -15204.613 -2072276 -15323.138  38.997678
9  -2072545 -14402.596 -2072450 -14513.219  32.918889
10 -2072189 -16022.434 -2072098 -16129.106  33.834074
11 -2071991 -16856.058 -2071928 -16942.976   6.277554
12 -2068407 -12999.396 -2068296 -13133.170   6.579285
13 -2069870 -12613.813 -2069764 -12731.848   2.357631
14 -2067635 -13188.253 -2067517 -13337.765  11.604519
15 -2066931 -13377.110 -2066809 -13518.753  22.719625
16 -2067411 -15084.692 -2067313 -15190.924  41.907273
17 -2066795 -18714.093 -2066741 -18846.019  14.541358
18 -2066384 -17080.538 -2066299 -17212.464  26.717495
19 -2068634 -19742.339 -2068580 -19835.464  27.483654
20 -2053326 -16930.710 -2053276 -17226.351  65.746074
21 -2051797 -17321.500 -2051899 -17579.762 227.516944
22 -2068307   2826.921 -2068066   2638.276  12.587853
23 -2067543   2648.205 -2067328   2449.631  37.729747
24 -2067126   4276.510 -2066904   4081.246  46.774630
25 -2066748   4170.604 -2066527   4001.816  59.509843
26 -2066068   2292.295 -2065860   2094.699  46.681553
27 -2065337   2107.872 -2065126   1900.397  43.386956
28 -2064606   1913.570 -2064378   1692.922  26.460389
29 -2064199   3558.561 -2063961   3356.401  47.742696
30 -2037464   6512.455 -2037076   5864.398  50.762994
31 -2036722   6825.682 -2036338   6199.227  22.699467
32 -2036876   6366.642 -2036498   5742.888  22.120176
33 -2040225   7150.180 -2039706   6575.029 161.631199
34 -2041064   7144.779 -2040732   6569.629  26.657582
35 -2044702 -15548.033 -2044564 -16024.903  23.844817
36 -2043992 -15723.521 -2043824 -16223.282  48.063840
37 -2043790 -14907.119 -2043611 -15383.990  34.844851
38 -2040616 -14820.445 -2040453 -15349.974  21.196233
39 -2039595 -15081.427 -2039485 -15588.263  47.263287

The Root-Mean-Square-Error (RMSE) for the fitted transform (in meters) is:

The output from v.transform on the same set of control points:

Transformation Matrix
| xoff a b |
| yoff d e |
-------------------------------------------
5301.399323 1.002469 0.009172 
-28155.882288 -0.013530 0.997547 
-------------------------------------------

full output including the residuals:

               CHECK MAP RESIDUALS
                Current Map                  New Map
 POINT      X coord    Y coord  |        X coord   Y coord    |      residuals

  1.   -2078417.36    -14810.57 |  -2078314.07      -14838.38 |        46.81
  2.   -2078743.11    -16057.95 |  -2078635.85      -16081.79 |        62.22
  3.   -2077261.34    -16435.35 |  -2077169.97      -16463.16 |        41.05
  4.   -2076709.16    -14405.37 |  -2076605.87      -14433.18 |        29.59
  5.   -2074178.76    -15830.25 |  -2074083.67      -15901.56 |        33.21
  6.   -2073849.93    -15707.44 |  -2073762.78      -15798.55 |        37.42
  7.   -2073449.80    -13873.17 |  -2073358.68      -13920.71 |        21.62
  8.   -2072358.86    -15204.61 |  -2072275.89      -15323.14 |        39.02
  9.   -2072544.55    -14402.60 |  -2072449.73      -14513.22 |        32.97
 10.   -2072188.97    -16022.43 |  -2072098.11      -16129.11 |        33.87
 11.   -2071991.43    -16856.06 |  -2071928.22      -16942.98 |         6.27
 12.   -2068406.55    -12999.40 |  -2068296.38      -13133.17 |         6.60
 13.   -2069870.19    -12613.81 |  -2069763.96      -12731.85 |         2.33
 14.   -2067635.38    -13188.25 |  -2067517.35      -13337.76 |        11.63
 15.   -2066931.10    -13377.11 |  -2066809.13      -13518.75 |        22.74
 16.   -2067411.11    -15084.69 |  -2067312.75      -15190.92 |        41.93
 17.   -2066795.16    -18714.09 |  -2066740.84      -18846.02 |        14.64
 18.   -2066383.87    -17080.54 |  -2066298.50      -17212.46 |        26.74
 19.   -2068634.37    -19742.34 |  -2068580.05      -19835.46 |        27.53
 20.   -2053326.48    -16930.71 |  -2053275.51      -17226.35 |        66.09
 21.   -2051797.30    -17321.50 |  -2051899.25      -17579.76 |       227.91
 22.   -2068307.24      2826.92 |  -2068065.64        2638.28 |        12.41
 23.   -2067542.73      2648.21 |  -2067327.61        2449.63 |        37.44
 24.   -2067125.72      4276.51 |  -2066903.98        4081.25 |        46.40
 25.   -2066748.43      4170.60 |  -2066526.69        4001.82 |        59.12
 26.   -2066067.79      2292.29 |  -2065860.31        2094.70 |        46.35
 27.   -2065336.69      2107.87 |  -2065125.92        1900.40 |        43.07
 28.   -2064605.58      1913.57 |  -2064378.35        1692.92 |        26.16
 29.   -2064199.15      3558.56 |  -2063961.13        3356.40 |        47.43
 30.   -2037464.39      6512.45 |  -2037075.56        5864.40 |        50.66
 31.   -2036721.82      6825.68 |  -2036338.39        6199.23 |        22.54
 32.   -2036875.74      6366.64 |  -2036497.71        5742.89 |        21.95
 33.   -2040224.67      7150.18 |  -2039706.23        6575.03 |       161.54
 34.   -2041064.45      7144.78 |  -2040732.32        6569.63 |        26.74
 35.   -2044701.68    -15548.03 |  -2044564.34      -16024.90 |        23.64
 36.   -2043992.10    -15723.52 |  -2043824.24      -16223.28 |        47.60
 37.   -2043789.90    -14907.12 |  -2043610.60      -15383.99 |        34.35
 38.   -2040615.94    -14820.44 |  -2040453.30      -15349.97 |        20.77
 39.   -2039594.70    -15081.43 |  -2039485.02      -15588.26 |        47.85


  Number of points: 39
  Residual mean average: 57.082951