cv.seqlr.batting: Cross Validation for Sequential BATTing
In SubgrpID: Patient Subgroup Identification for Clinical Drug Development

Cross Validation for Sequential BATTing

cv.seqlr.batting(y, x, censor.vec = NULL, trt.vec = NULL, trtref = NULL,
  type = "c", n.boot = 50, des.res = "larger", class.wt = c(1, 1),
  min.sigp.prcnt = 0.2, 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 predictors
`censor.vec`	vector giving the censor status (only for TTE data , censor=0,event=1) : default = NULL
`trt.vec`	vector containing values of treatment variable ( for predictive signature). Set trt.vec to NULL for prognostic signature.
`trtref`	code for treatment arm.
`type`	data type. "c" - continuous , "b" - binary, "s" - time to event : default = "c".
`n.boot`	number of bootstraps in BATTing step.
`des.res`	the desired response. "larger": prefer larger response. "smaller": prefer smaller response
`class.wt`	vector of length 2 used to weight the accuracy score , useful when there is class imbalance in binary data defaults to c(1,1)
`min.sigp.prcnt`	desired proportion of signature positive group size for a given cutoff.
`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, "unicart": univariate rpart filtering for prognostic case.
`k.fold`	number of folds for CV.
`cv.iter`	algorithm terminates after cv.iter successful iterations of cross-validation.
`max.iter`	total number of iterations allowed (including unsuccessful ones).

a list containing 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