ordinalDI: Fits dose interval using ordinal approach
In lixiaomao/personalized-dosing-interval: Determining Personalized Dosing Interval

Description Usage Arguments Examples

This function is the wrapper of fitting functions for doseInt

ordinalDI(train, type = "PDI", two.sided = FALSE, cost = 1, K = 10,
  continuous = FALSE, aL = NULL, aU = NULL, global = F, margin = 0,
  alpha = c(0.5, 0.5), breaks = "quantile", lower = TRUE,
  method = "svmLinear", ...)

`train`	the training data set.
`type`	indicates whether PDI or EDI is to be calculated.
`two.sided`	indicator of whether two-sided interval is considered.
`K`	level of ordinal treatment, default 10
`global`	if global data are used for ordinal approach, default False
`margin`	how large is the margin if global=False, default 0
`alpha`	the guaranteed probability in PDI and the relative loss in EDI. Default is c(0.5,0.5).
`breaks`	ways 'as.ordinal' cuts continuous treatment into several ordinal levels, default 'quantile'. User can also choose 'uniform' or specify customized breaks
`lower`	True or False, Whether lower boundary is considered when applied to one-sided interval
`method`	specified methods used for DC and ordinal approaches.
`test`	the testing data set. If test is NULL, use train as test.
`pred0`	the initial prediction for training data set, if NULL, default initialization is used
`nfolds`	the number of folds used in cross-validation.
`family`	specifis the methods. 'continuous' leads to DC-algorithm,'ordinal' handles the ordinal treatments, while 'as.ordinal' cuts continuous treatment into several ordinal levels and apply the ordinal algorithm.
`trace`	the level of detail to be printed: 1,2,3,4
`Cost`	cost for the support vector machine

  fit1=ordinalDI(train1,alpha=c(0.5,0.5),K=20,continuous = T)
  predNew1=predict(fit1,test1)
  1-mean((predNew1$value-test1$opt)^2)/var(test1$opt)

  fit3=ordinalDI(train3,two.sided=T,alpha=c(0.5,0.5),K=20,continuous=T)
  predNew3=predict(fit3,test3)
  cor(predNew3$value_L,test3$opt_L)^2
  cor(predNew3$value_R,test3$opt_R)^2