Fuzzy methods throw warnings about data range

[nebfield]

Hello,

Every fuzzy method I've tried has thrown lots of the following warning: There are your newdata which are out of the specified range. By chance I found a day-old stackexchange post about this problem. The problem appears to be that when range.data is calculated and cross validation is used new data from different folds can fall outside the precalculated range.

When checking the output of $fit from getModelInfo("FRBCS.W") I spotted the following code:

args$range.data <- apply(x, 2, extendrange)

Using the frbs.learn function directly allows range.data to be passed as a parameter. A possible solution would be range.data to be a parameter for train. This way a user could calculate the range.data from the entire dataset, as suggested in the stackexchange post. Below is an MRE.

Thanks!

Minimal, reproducible example:

Minimal dataset:

library(caret)
set.seed(0451)
data(iris)
irisShuffled <- iris[sample(nrow(iris)),]

inTrain <- createDataPartition(y = irisShuffled$Species,
                               p = .75,
                               list = FALSE)

training <- irisShuffled[inTrain,]
testing <- irisShuffled[-inTrain,]

ctrl <- trainControl(method = "repeatedcv",
                     repeats = 3)

Minimal, runnable code:

fuzzyFit <- train(Species ~ .,
                  data = training,
                  method = "FRBCS.W",
                  trControl = ctrl)

> There were 27 warnings (use warnings() to see them)
Warning messages:
1: In validate.params(object, newdata) :
  There are your newdata which are out of the specified range

Session Info:

> sessionInfo()
R version 3.3.1 (2016-06-21)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 16.04.1 LTS

locale:
 [1] LC_CTYPE=en_GB.UTF-8       LC_NUMERIC=C               LC_TIME=en_GB.UTF-8       
 [4] LC_COLLATE=en_GB.UTF-8     LC_MONETARY=en_GB.UTF-8    LC_MESSAGES=en_GB.UTF-8   
 [7] LC_PAPER=en_GB.UTF-8       LC_NAME=C                  LC_ADDRESS=C              
[10] LC_TELEPHONE=C             LC_MEASUREMENT=en_GB.UTF-8 LC_IDENTIFICATION=C       

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

other attached packages:
[1] frbs_3.1-0      caret_6.0-71    ggplot2_2.1.0   lattice_0.20-34

loaded via a namespace (and not attached):
 [1] Rcpp_0.12.7        magrittr_1.5       splines_3.3.1      MASS_7.3-45       
 [5] munsell_0.4.3      colorspace_1.2-6   foreach_1.4.3      minqa_1.2.4       
 [9] stringr_1.1.0      car_2.1-3          plyr_1.8.4         tools_3.3.1       
[13] nnet_7.3-12        pbkrtest_0.4-6     parallel_3.3.1     grid_3.3.1        
[17] gtable_0.2.0       nlme_3.1-128       mgcv_1.8-15        quantreg_5.29     
[21] e1071_1.6-7        class_7.3-14       MatrixModels_0.4-1 iterators_1.0.8   
[25] lme4_1.1-12        Matrix_1.2-7.1     nloptr_1.0.4       reshape2_1.4.1    
[29] codetools_0.2-14   rsconnect_0.4.3    stringi_1.1.1      compiler_3.3.1    
[33] scales_0.4.0       stats4_3.3.1       SparseM_1.72     

[topepo]

I can make a change to the code such that, if you pass range.data to train it will be used. If not, it can follow the current process.

This probably affects other models too.

[topepo]

Can you test these on your data?