flars_TrainTest: Internal function for doing simulation using functional lars.
In flars: Functional LARS

This is a function built for doing data generation and variable selection using functional lars with different settings and data with different correlation structures.

flars_TrainTest(seed=1,nsamples=120,nTrain=80,var_type=c('f','m'),
                VarThreshold0=0.1,SignThreshold0=0.8,cor_type=1:5,
                lasso=TRUE, check = 1,uncorr=T,nVar=8,Discrete_Norm_ID=1:12,
                NoRaw_max=12,raw_max=9,hyper=NULL,RealX=NULL,RealY=NULL,
                dataL=NULL,nCor=0,control=list())

`seed`	Set the seed for random numbers.
`nsamples`	Sample size of the data to generate.
`nTrain`	Sample size of the training data.
`var_type`	Two choices of the variable types. See details for more information.
`cor_type`	Correlation structures. See details for more information.
`VarThreshold0`	Threshold for removing variables based on variation explained. See `flars` for more details.
`SignThreshold0`	Same as `VarThreshold0`
`lasso`	Use lasso modification or not. In other words, can variables selected in the former iterations be removed in the later iterations.
`check`	Type of lasso check. 1 means variance check, 2 means sign check. `check=1` is much better than the other one.
`uncorr`	If the variables are uncorrelated or not. See details for more information.
`nVar`	Number of variables to generate.
`Discrete_Norm_ID`	Which discrete method and which norm to use. 1 to 12.
`NoRaw_max`	Number of variables to select when not using RDP discretising method.
`raw_max`	Number of variables to select when using RDP discretising method.
`hyper`	Hyper parameters used in the Gaussian process. GP is used for building the covariance structure of the functional variables.
`RealX`	Real data input X.
`RealY`	Real data input Y.
`dataL`	Real input data list rather than generate in the function. It should has the same structure as that generated.
`nCor`	Number of cores to use.
`control`	List of control items. See `fccaGen` for more details.