Dear all,

I did a 5-repeat of 10-fold cross validation using partial least square
regression model provided by caret package. Can anyone tell me how are the
values in plsTune$resample calculated? Is that predicted on each hold-out
set using the model which is trained on the rest data with the optimized
parameter tuned from previous cross validation? So in the following
example, firstly, 5-repeat of 10-fold cross validation gives 2 for ncomp as
the best, and then using ncomp of 2 and the training data to build a model
and then predict the hold-out data with the model to give a RMSE and
RSQUARE - is what I am thinking true?


> plsTune
524 samples
615 predictors

Pre-processing: centered, scaled
Resampling: Cross-Validation (10 fold, repeated 5 times)

Summary of sample sizes: 472, 472, 471, 471, 471, 471, ...

Resampling results across tuning parameters:

  ncomp  RMSE  Rsquared  RMSE SD  Rsquared SD
  1      16.8  0.434     1.47     0.0616
  2      14.3  0.612     2.21     0.0768
  3      13.5  0.704     6.33     0.145
  4      14.6  0.706     9.29     0.163
  5      15.2  0.703     10.9     0.172
  6      16.5  0.69      13.4     0.181
  7      18.4  0.672     17.8     0.194
  8      20    0.651     20.4     0.199
  9      20.9  0.634     20.9     0.199
  10     22.1  0.613     22.1     0.197
  11     23.3  0.599     23.8     0.198
  12     24    0.588     24.7     0.198
  13     24.9  0.572     25.2     0.197
  14     25.8  0.557     26.2     0.194
  15     26.2  0.544     25.8     0.191
  16     26.6  0.532     25.5     0.187

RMSE was used to select the optimal model using  the one SE rule.
The final value used for the model was ncomp = 2.
>
> plsTune$resample
   ncomp     RMSE  Rsquared    Resample
1      2 13.61569 0.6349700 Fold06.Rep4
2      2 16.02091 0.5808985 Fold05.Rep1
3      2 12.59985 0.6008357 Fold03.Rep5
4      2 13.20069 0.6296245 Fold02.Rep3
5      2 12.43419 0.6560434 Fold04.Rep2
6      2 15.36510 0.5954177 Fold04.Rep5
7      2 12.70028 0.6894489 Fold03.Rep2
8      2 13.34882 0.6468300 Fold09.Rep3
9      2 14.80217 0.5575010 Fold08.Rep3
10     2 19.03705 0.4907630 Fold05.Rep4
11     2 14.26704 0.6579390 Fold10.Rep2
12     2 13.79060 0.5806663 Fold05.Rep3
13     2 14.83641 0.5918039 Fold05.Rep2
14     2 12.48721 0.7011439 Fold01.Rep3
15     2 14.98765 0.5866102 Fold07.Rep4
16     2 10.88100 0.7597167 Fold06.Rep1
17     2 13.60705 0.6321377 Fold08.Rep5
18     2 13.42618 0.6136031 Fold08.Rep4
19     2 13.26066 0.6784586 Fold07.Rep1
20     2 13.20623 0.6812341 Fold03.Rep3
21     2 18.54275 0.4404729 Fold08.Rep2
22     2 11.80312 0.7177681 Fold05.Rep5
23     2 18.56271 0.4661072 Fold03.Rep1
24     2 13.54879 0.5850439 Fold10.Rep3
25     2 14.10859 0.5994811 Fold06.Rep5
26     2 13.68329 0.6701091 Fold01.Rep5
27     2 16.12123 0.5401200 Fold10.Rep1
28     2 12.92250 0.6917220 Fold06.Rep3
29     2 12.94366 0.6400066 Fold06.Rep2
30     2 12.39889 0.6790578 Fold01.Rep2
31     2 13.48499 0.6759649 Fold01.Rep1
32     2 12.52938 0.6728476 Fold03.Rep4
33     2 16.43352 0.5795160 Fold09.Rep5
34     2 12.53991 0.6550694 Fold09.Rep4
35     2 12.78708 0.6304606 Fold08.Rep1
36     2 13.97559 0.6655688 Fold04.Rep3
37     2 15.31642 0.5124997 Fold09.Rep2
38     2 15.24194 0.5324943 Fold09.Rep1
39     2 12.90107 0.6318960 Fold04.Rep1
40     2 13.59574 0.6277869 Fold01.Rep4
41     2 19.73633 0.4154821 Fold07.Rep5
42     2 12.03759 0.6537381 Fold02.Rep5
43     2 15.47139 0.5597097 Fold02.Rep4
44     2 22.55060 0.3816672 Fold07.Rep3
45     2 14.57875 0.6269560 Fold07.Rep2
46     2 13.02385 0.6395148 Fold02.Rep2
47     2 13.81020 0.6116137 Fold02.Rep1
48     2 13.46100 0.6200828 Fold04.Rep4
49     2 13.95487 0.6709253 Fold10.Rep5
50     2 12.65981 0.6606435 Fold10.Rep4

Best,
Zhenjiang

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