Usage
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())
Arguments
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.
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.