cv.aim.batting: The function for CV in aim.batting
In SubgrpID: Patient Subgroup Identification for Clinical Drug Development

Implements k-fold cross validation for aim.batting.

cv.aim.batting(y, x, censor.vec = NULL, trt.vec = NULL, trtref = NULL,
  type, n.boot, des.res = "larger", min.sigp.prcnt = 0.2, mc.iter = 1,
  mincut = 0.1, pre.filter = NULL, filter.method = NULL, k.fold = 5,
  cv.iter = 50, max.iter = 500)

`y`	data frame containing the response
`x`	data frame containing the predictor
`censor.vec`	data frame giving the censor status (only for TTE data , censor=0,event=1) - default = NULL
`trt.vec`	data frame giving the censor status (only for TTE data , censor=0,event=1) - default = NULL
`trtref`	treatment reference indicator: 1=treatment, 0=control
`type`	data type - "c" - continuous , "b" - binary, "s" - time to event - default = "c"
`n.boot`	number of bootstraps in bootstrapping step.
`des.res`	the desired response. "larger": prefer larger response. "smaller": prefer smaller response
`min.sigp.prcnt`	desired proportion of signature positive group size for a given cutoff.
`mc.iter`	# of iterations for the MC procedure to get a stable "best number of predictors"
`mincut`	the minimum cutting proportion for the binary rule at either end. It typically is between 0 and 0.2. It is the parameter in the functions of AIM package.
`pre.filter`	NULL, no prefiltering conducted;"opt", optimized number of predictors selected; An integer: min(opt, integer) of predictors selected
`filter.method`	NULL, no prefiltering, "univariate", univaraite filtering; "glmnet", glmnet filtering
`k.fold`	# cross-validation folds
`cv.iter`	Algotithm terminates after cv.iter successful iterations of cross-validation
`max.iter`	total # iterations (including unsuccessful) allowed.

"cv.aim.batting" returns a list with following entries:

`stats.summary`	Summary of performance statistics.
`pred.classes`	Data frame containing the predictive clases (TRUE/FALSE) for each iteration.
`folds`	Data frame containing the fold indices (index of the fold for each row) for each iteration.
`sig.list`	List of length cv.iter * k.fold containing the signature generated at each of the k folds, for all iterations.
`error.log`	List of any error messages that are returned at an iteration.
`interplot`	Treatment*subgroup interaction plot for predictive case