title: Extracting PCA axes for further analysis
body: <p>I am analysing data regarding reed fields. Variables I have measured are water depth, reed height, reed density, etc. As some of the variables are dependent, I performed a PCA in order to reduce these variables to 2 PCA-axes (N=104).</p>
  
  <p>For executing the PCA I used the <code>vegan</code> package in R. My data looks like this: </p>
  
  <pre><code>row.names   Waterpeil   hoogte_max  Som Leeftijd_riet   PFD oppervlakte onderlaag_num   afst_rand
  1   1   5   2.5 51  0.15686274  1.616921    8.127192    2   24.154590
  2   3   9   2.5 44  0.13636364  1.564643    9.023642    2   8.349288
  3   4   0   2.5 84  0.30952381  1.352548    8.498775    2   26.226896
  4   5   0   3.5 58  0.43103448  1.384183    9.301617    1   57.320000
  5   6   40  2.5 52  0.42307692  1.361262    10.316058   1   45.470000
  6   7   5   3.0 19  0.00000000  1.429287    9.927788    1   36.720000
  7   9   0   2.5 64  0.28125000  1.355100    8.029911    2   19.560000
  8   11  120 3.5 29  0.03448276  1.336117    11.147484   1   252.630000
  9   14  0   2.0 27  0.07407407  1.847756    7.445060    2   1.864342
  10  16  20  2.5 57  0.24561404  1.582308    8.425177    2   9.490196
  11  17  5   3.0 54  0.01851852  1.348305    9.315008    2   15.960000
  12  18  0   1.5 5   1.00000000  1.643657    8.063648    2   6.526300
  13  21  0   2.0 18  0.05555556  1.394964    8.752185    2   37.576955
  14  22  20  2.0 48  0.16666667  1.617045    8.911028    1   11.592383
  15  25  0   2.5 71  0.42253521  1.749114    7.271499    2   6.572772
  16  26  0   2.0 50  0.30000000  1.464582    7.349908    2   9.849276
  17  27  5   2.5 61  0.34426229  1.511217    8.379012    2   14.082827
  18  28  5   2.0 123 0.06504065  1.538188    8.271017    2   11.658142
  19  29  100 3.0 75  0.44000000  1.896483    7.968603    1   9.071897
  20  30  100 3.0 95  0.55789474  1.768147    8.367626    1   2.300783
  21  32  0   3.0 74  0.45945946  1.458793    9.453464    2   57.210000
  22  33  15  3.0 66  0.24242424  1.572704    7.620507    1   8.700000
  23  34  5   3.0 83  0.38554217  1.436063    11.636262   1   50.613265
  24  35  5   2.5 58  0.31034483  1.313440    9.370347    2   52.605041
  25  36  20  2.5 91  0.28571429  1.544032    8.451961    1   9.713351
  26  37  10  2.5 34  0.23529412  1.524725    9.348687    2   6.920026
  27  38  20  2.5 48  0.41666667  1.584892    7.780915    1   11.302639
  28  39  40  2.5 51  0.15686274  1.535552    6.994035    1   18.999423
  29  40  35  2.5 48  0.45833333  1.460579    9.073331    1   12.869075
  30  41  5   3.0 58  0.43103448  1.747669    7.628542    2   3.860225
  31  42  25  2.5 36  0.52777778
  </code></pre>
  
  <p>I have done this, this is the output for the first two axes:</p>
  
  <pre><code>y&lt;-rda(nestendca2) 
  summary(y)
                 PC1       PC2    
  Waterpeil     13.816422 -2.312641
  hoogte_max     0.094747 -0.014497 
  Som            2.955029 10.812549  
  Leeftijd_riet  0.016476  0.019629  
  PFD            0.007361 -0.003386  
  oppervlakte    0.052943  0.039657 
  </code></pre>
  
  <p>Now I want to implement these two axes in a logistic regression, relating it to breeding success of a bird of prey that breeds in these fields.</p>
  
  <p>How can I do this?</p>