aim.rule.batting: The main AIM-Rule-BATTing function
In SubgrpID: Patient Subgroup Identification for Clinical Drug Development

This function first uses AIM to get the candidate rules and then applies Sequential BATTing to get the best rule(s).

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aim.rule.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)

`y`	data frame of the response variable.
`x`	data frame of predictors, each column of which corresponds to a variable.
`censor.vec`	data frame indicating censoring for survival data. For binary or continuous data, set censor.vec <- NULL.
`trt.vec`	data frame indicating whether or not the patient was treated. For the pronostic case, set trt.vec <- NULL.
`trtref`	code for treatment arm.
`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.
`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", univariate filtering; "glmnet", glmnet filtering; "unicart", CART filtering (only for prognostic case).