R: caret and nnet Error with big data

Question

i've a problem. I've a dataset a lot of features. When i try to perform my nnet with caret R give me back an error. If i try to perform a smaller part of the features, nnet converge.

Here is my code:

> dim(trainT)
[1]  130 3413
> nnFit <- train(target ~ ., data = trainT,
+ method = "nnet",
+                trControl = fitControl#,
+                #trControl = ctrl, metric = "ROC", 
+                #verbose = TRUE#,
+                #tuneGrid = nnGrid
+ )
Something is wrong; all the Accuracy metric values are missing:
    Accuracy       Kappa    
 Min.   : NA   Min.   : NA  
 1st Qu.: NA   1st Qu.: NA  
 Median : NA   Median : NA  
 Mean   :NaN   Mean   :NaN  
 3rd Qu.: NA   3rd Qu.: NA  
 Max.   : NA   Max.   : NA  
 NA's   :9     NA's   :9    
Error in train.default(x, y, weights = w, ...) : Stopping
In addition: There were 50 or more warnings (use warnings() to see the first 50)
>
> nnFit <- train(target ~ ., data = trainT[,1:100],
               method = "nnet",
               trControl = fitControl#,
               #trControl = ctrl, metric = "ROC", 
               #verbose = TRUE#,
               #tuneGrid = nnGrid
)
# weights:  102
initial  value 65.440715 
iter  10 value 34.586483
iter  20 value 25.531746
iter  30 value 22.930604
iter  40 value 22.919387
iter  50 value 20.326238
iter  60 value 20.018595
iter  70 value 5.289718
iter  80 value 0.016055
final  value 0.000063 
converged
# weights:  304
initial  value 85.540457 
iter  10 value 25.219303
iter  20 value 5.562977
iter  30 value 4.712105
iter  40 value 4.676887
iter  50 value 4.625627
iter  60 value 4.622304
iter  70 value 4.597801
iter  80 value 4.582877
iter  90 value 4.570602
iter 100 value 4.569542
final  value 4.569542 
stopped after 100 iterations
[...]
initial  value 75.037558 
iter  10 value 4.301843
iter  20 value 1.495044
iter  30 value 0.159978
iter  40 value 0.118735
iter  50 value 0.110560
iter  60 value 0.101595
iter  70 value 0.079860
iter  80 value 0.073034
iter  90 value 0.065459
iter 100 value 0.052024
final  value 0.052024 
stopped after 100 iterations
# weights:  506
initial  value 95.448738 
iter  10 value 20.859400
iter  20 value 6.493820
iter  30 value 5.597509
iter  40 value 5.516322
iter  50 value 5.510970
iter  60 value 5.510881
final  value 5.510881 
converged

Can you help me? :)

PS: Session info:

> sessionInfo()
R version 3.2.0 (2015-04-16)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 8 x64 (build 9200)

locale:
[1] LC_COLLATE=Italian_Italy.1252  LC_CTYPE=Italian_Italy.1252   
[3] LC_MONETARY=Italian_Italy.1252 LC_NUMERIC=C                  
[5] LC_TIME=Italian_Italy.1252    

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] nnet_7.3-9      caret_6.0-47    ggplot2_1.0.1   lattice_0.20-31

loaded via a namespace (and not attached):
 [1] Rcpp_0.11.6         magrittr_1.5        splines_3.2.0       MASS_7.3-40        
 [5] munsell_0.4.2       colorspace_1.2-6    foreach_1.4.2       minqa_1.2.4        
 [9] car_2.0-25          stringr_1.0.0       plyr_1.8.2          tools_3.2.0        
[13] parallel_3.2.0      pbkrtest_0.4-2      grid_3.2.0          gtable_0.1.2       
[17] nlme_3.1-120        mgcv_1.8-6          quantreg_5.11       e1071_1.6-4        
[21] class_7.3-12        iterators_1.0.7     gtools_3.5.0        lme4_1.1-7         
[25] digest_0.6.8        Matrix_1.2-0        nloptr_1.0.4        reshape2_1.4.1     
[29] codetools_0.2-11    stringi_0.4-1       compiler_3.2.0      BradleyTerry2_1.0-6
[33] scales_0.2.4        SparseM_1.6         brglm_0.5-9         proto_0.3-10  

EDIT: i forget a comma in my code :( i'm while only the col and only for test.

@cyberj0g:

I try what you suggested:

1- analyzing the summary that I have seen are all numbers.

2- If I call warning () does not return anything, but if I try to stop before completing the nnet me:

        > nnFit <- train(target ~ ., data = trainT,
    +                method = "nnet",
    +                trControl = fitControl#,
    +                #trControl = ctrl, metric = "ROC", 
    +                #verbose = TRUE#,
    +                #tuneGrid = nnGrid
    + )

    Warning messages:
    1: In eval(expr, envir, enclos) :
      model fit failed for Fold1.Rep1: size=1, decay=0e+00 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
      too many (3011) weights

    2: In eval(expr, envir, enclos) :
      model fit failed for Fold1.Rep1: size=3, decay=0e+00 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
      too many (9031) weights

    3: In eval(expr, envir, enclos) :
      model fit failed for Fold1.Rep1: size=5, decay=0e+00 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
      too many (15051) weights

    4: In eval(expr, envir, enclos) :
      model fit failed for Fold1.Rep1: size=1, decay=1e-01 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
      too many (3011) weights

    5: In eval(expr, envir, enclos) :
      model fit failed for Fold1.Rep1: size=3, decay=1e-01 Error in nnet.default(x, y, w, entropy = TRUE, ...) : 
      too many (9031) weights

3- if i increase the number of cv (if i understand well you refear to it) the problem is the same:

    > fitControl <- trainControl(## 5-fold CV
+   method = "repeatedcv",
+   number = 1000,
+   ## repeated 5 times
+   repeats = 5)
> nnFit <- train(target ~ ., data = trainT,
+                method = "nnet",
+                trControl = fitControl#,
+                #trControl = ctrl, metric = "ROC", 
+                #verbose = TRUE#,
+                #tuneGrid = nnGrid
+ )

There were 50 or more warnings (use warnings() to see the first 50)

Answer 1

It's not clear what causing the error, but I suggest following:

1. Check your data for abnormalities: summary(trainT)
2. Check warnings after error: warnings()
3. Try to increase the number of iterations: trainControl(number=1000)

Also, your full dataset contains barely enough samples to train a model with 130 predictors (it depends, though). Convergence on only 100 samples most likely means nothing.