knn:
 [1]  0.80741980 -0.04301991 -0.30734360  0.16768600  0.10454060  0.52660540
 [7]  0.77929730  0.42215750  0.82379380  1.28303400

knncv:
             [,1]       [,2]       [,3]       [,4]         [,5]
 [1,]  0.96253580  0.8290678  0.7035998  0.5766590  0.600971023
 [2,] -0.08709228 -0.2363891 -0.2368864  0.1509417  0.308018348
 [3,] -0.28456610 -0.2947291 -0.1148392 -0.1138031  0.137777232
 [4,]  1.14349700  1.1147553  1.0900888  0.9844730  0.767039373
 [5,] -0.62426970 -0.5018562 -0.3917204 -0.3983415  0.182015707
 [6,]  0.65751130  0.7279118  0.6761769  0.6671159  0.685262020
 [7,]  1.10188000  0.9641244  1.0077154  0.9241669  0.762766925
 [8,]  0.52660540  0.6193751  0.6281908  0.4316038  0.470013992
 [9,]  1.00501700  0.9308267  1.0065332  0.9292795  0.752825835
[10,] -0.86213930 -0.7259564 -0.7112580 -0.4878214 -0.002805358

knnsel:
 [1]  0.7152207 -0.2282846 -0.2828626  0.2149915  0.2159573  0.4153593
 [7]  0.6558377  0.6320193  0.9514354  1.2432110

knncombo:
 [1]  0.7565039 -0.1977091 -0.3569782  0.2426838  0.1849690  0.4967441
 [7]  0.7320462  0.6387148  0.9253139  1.2595731

Average squared errors on test set:
                                                           knnsel   knncombo 
0.04307471 0.03069640 0.03078074 0.04916121 0.16349156 0.03069640 0.02700341 

knnsel$cv.sq.err:
[1] 0.03186561 0.02765499 0.02801619 0.04500912 0.13268475

knnsel$k:
[1] 3

knncombo$beta:
 (Intercept)        p.cv1        p.cv2        p.cv3        p.cv4        p.cv5 
 0.006151121  0.356122218  0.138670457  0.375707101  0.286272531 -0.170580457