r - error in createFolds cut.default while running train() in caret with multiple binomial target variables -


this problem has same symptoms train() in caret package returns error names & gsub solution described doesn't apply here far can see.

i have 8 binomial target variables , 12 predictor variables (actually, 577 predictor variables, i've included 12 in minimal example). predictor variables have same number of positive cases:

> require(caret) > head(eg.data)   bottle cat chair face house scissors scrambledpix shoe      x1 1      0   0     0    0     0    1        0    0 1.282427535 2      0   0     0    0     0    1        0    0 2.580423598 3      0   0     0    0     0    1        0    0 2.757994797 4      0   0     0    0     0    1        0    0 2.027544189 5      0   0     0    0     0    1        0    0 2.011910591 6      0   0     0    0     0    1        0    0 1.381372427           x2         x3         x4      x5      x6 1  0.56927127535 -0.41445500589  0.05883449623 1.325428161 3.009461590 2  0.99142631615 -0.29943837061  0.07639494922 1.523704820 2.827368769 3  2.03652352150 -0.17050305555 -0.31151493933 1.573253408 2.678044808 4  1.25721256063 -0.13619253754  0.51253133255 2.577229617 1.928547094 5 -0.08773097125  0.06366970261  0.39996831088 1.887088568 1.946206958 6 -0.25631254599 -0.02384295467  0.46782728851 1.200404398 1.325037590          x7        x8        x9        x10 1 0.06590922936  0.6734459904 -0.5028127515 -0.88796906295 2 1.74129314357 -0.7760203940  0.2435879550  0.96297913339 3 2.33400909898  0.0439339562  1.0221119115  0.07875704254 4 2.65188422088 -0.1230319426  1.6562415384  0.18348716525 5 1.69440143996  0.6049393761  1.0446174220  0.87828319489 6 1.43499026729 -0.2976883919  0.7316561774  0.43665437272         x11       x12 1 -1.7844737347 -2.1649063167 2  0.2034972031 -1.7478010604 3  0.9186460991 -0.3217861157 4  1.3983604989 -1.4887151593 5  1.0934001840 -1.8538057112 6  0.8168093363 -0.6653136097  > #all columns have different values specified > apply(eg.data,2,function(col){return(length(unique(col)))})       bottle          cat        chair         face        house         2            2            2            2            2      scissors scrambledpix         shoe           x1           x2         2            2            2          864          864        x3           x4           x5           x6           x7       864          863          864          864          864        x8           x9          x10          x11          x12       864          864          863          864          863  > apply(eg.data[,1:8],2,table)   bottle cat chair face house scissors scrambledpix shoe 0    756 756   756  756   756      756          756  756 1    108 108   108  108   108      108          108  108

i try run train() caret. hope use neuralnet method, (i think) requires target variables formatted collection of binomial variables, i'm trying svmlinear, works test case. 

> res <- train(train.formula, +              eg.data, +              method = "svmlinear", +              trcontrol = traincontrol(method="cv", number=10)) error in cut.default(y, unique(quantile(y, probs = seq(0, 1, length = cuts))),  :    invalid number of intervals

running traceback:

> traceback() 8: stop("invalid number of intervals") 7: cut.default(y, unique(quantile(y, probs = seq(0, 1, length = cuts))),         include.lowest = true) 6: cut(y, unique(quantile(y, probs = seq(0, 1, length = cuts))),         include.lowest = true) 5: createfolds(y, trcontrol$number, returntrain = true) 4: train.default(x, y, weights = w, ...) 3: train(x, y, weights = w, ...) 2: train.formula(train.formula, eg.data, method = "svmlinear", trcontrol = traincontrol(method = "cv",         number = 10)) 1: train(train.formula, eg.data, method = "svmlinear", trcontrol = traincontrol(method = "cv",         number = 10))

as can see seems problem similar reported before, there no na or nan values here, don't know solution be.

train requires outcome single dimensional factor (as opposed multiple binary outcomes). if separate outcomes (i.e. not mutually exclusive) need run 1 model per outcome.


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