tdmReadAndSplit: Read and split the task data.
In TDMR: Tuned Data Mining in R

Read the task data using tdmReadDataset and split them into a test part and a training/validation-part and return a TDMdata object.

1	tdmReadAndSplit(opts, tdm, nExp = 0, dset = NULL)

`opts`	a list from which we need here the elements `READ.INI`: [T] =T: do read and split, =F: return NULL `READ.*`: other settings for `tdmReadDataset` `filename`: needed for `tdmReadDataset` `filetest`: needed for `tdmReadDataset` `TST.testFrac`: [0.1] set this fraction of the daa aside for testing `TST.COL`: string with name for the partitioning column, if tdm$umode is not "SP_T". (If tdm$umode=="SP_T", then TST.COL="tdmSplit" is used.)
`tdm`	a list from which we need here the elements `mainFile`: if not NULL, set working dir to `dir(mainFile)` before executing `tdmReadDataset` `umode`: [ "RSUB" \| "CV" \| "TST" \| "SP_T" ], how to divide in training/validation data for tuning and test data for the unbiased runs `SPLIT.SEED`: if NULL, set random number generator (RNG) to `tdmRandomSeed` when constructing. `dataObj`. If not NULL, set RNG to SPLIT.SEED + nExp –> deterministic test set split `stratified`: [NULL] string specifying the column with the response variable for classification. If not NULL, do the split by stratified sampling (at least one record of each class level found in `dset[,tdm$stratified]` shall appear in the train-vali-set). Recommended for classification
`nExp`	[0] experiment counter, used to select a reproducible different seed, if `tdm$SPLIT.SEED!=NULL`
`dset`	[NULL] if non-NULL, reading of dset is skipped and the given data frame dset is used.

If dset is NULL, the files specified in opts are read into dset, see tdmReadDataset for details. Then, depending on the value of tdm$umode

"SP_T": split the data randomly into training and test data with test set fraction according to opts$TST.testFrac. Make use of tdm$SPLIT.SEED and tdm$stratified, if given. Set TST.COL to "tdmSplit".
"RSUB", "CV": use all data for training/validation. That is, the training-validation split is done later in tdmClassifyLoop or tdmRegressLoop.
"TST": split the data into training and test data according to column. opts$TST.COL (usually "TST.COL"), which carries a 1 for each test record and a 0 else. If opts$filetest is specified, then all records from this file will carry a 1 in opts$TST.COL. All records from opts$filename carry a 0.

dataObj, either NULL (if opts$READ.INI==FALSE) or an object of class TDMdata containing

`dset`	a data frame with the complete data set
`TST.COL`	string, the name of the column in `dset` which has a 1 for records belonging to the test set and a 0 for train/vali records. If tdm$umode=="SP_T", then TST.COL="tdmSplit", else TST.COL=opts$TST.COL.
`filename`	`opts$filename`, from where the data were read