CODEWITHSUNDEEP
rr-caretfeature-selection
Debutant
Debutant

Reputation: 357

"Error in seeds[[num_rs + 1L]] : subscript out of bounds" when using caret for creatng LVQ model?

I'm using caret package to create a LVQ model and select features on a dataset of 579 independent variable and 55 samples:

set.seed(123)
data=data
control <- trainControl(method="repeatedcv", number=5, repeats=10)

But when I run the command to train the model I get the following error:

model <- train(remission~., data=data, method="lvq", preProcess="scale", trControl=control, importance=T)
Error in seeds[[num_rs + 1L]] : subscript out of bounds

Can you suggest any solutions? Considering the number of variables I have, this seems the best way to find important features for my model. I even tried trimming my variables to 40 and 10, but I still get the same error.

Upvotes: 1

Views: 1021

Answers (1)

StupidWolf
StupidWolf

Reputation: 47008

The code to generate a grid runs into problems for a small dataset, you can look at the code under getModelInfo("lvq")$lvq$grid, also answered by the author of caret. You can provide your own grid and also note importance=TRUE is not an option for this:

library(multtest)
library(caret)
data(golub)
data = data.frame(t(golub))
data$cl=factor(golub.cl)

control <- trainControl(method="cv", number=5)

model <- train(cl~., data=data, method="lvq", preProcess="scale",trControl=control)

Error in seeds[[num_rs + 1L]] : subscript out of bounds

TG = expand.grid(k=1:3,size=seq(5,20,by=5))
model <- train(cl~., data=data, method="lvq", preProcess="scale",trControl=control,tuneGrid=TG)

Learning Vector Quantization 

  38 samples
3051 predictors
   2 classes: '0', '1' 

Pre-processing: scaled (3051) 
Resampling: Cross-Validated (5 fold) 
Summary of sample sizes: 31, 30, 31, 29, 31 
Resampling results across tuning parameters:

  k  size  Accuracy   Kappa    
  1   5    0.9527778  0.8967033
  1  10    1.0000000  1.0000000
  1  15    0.9492063  0.8929766
  1  20    0.9206349  0.8461538
  2   5    1.0000000  1.0000000
  2  10    0.9206349  0.8321070
  2  15    0.9555556  0.8800000
  2  20    0.9714286  0.9391304
  3   5    0.9492063  0.8929766
  3  10    0.9555556  0.9000000
  3  15    0.9777778  0.9538462
  3  20    0.9527778  0.8967033

Upvotes: 2

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