ANM_XGB: Additive noise model using XGBoost regression.

In DMIRLAB-Group/CANM: Causal Discovery with Cascade Nonlinear Additive Noise Models

Description

Additive noise model using the XGBoost regression.

Usage

ANM_XGB(x, y, booster = "gbtree", nrounds = 30, gamma = 0,
  nthread = 1, ...)

Arguments

x	The observation of the cause.
y	The observation of the effect.
booster	The regression function, "gbtree" or "gblinear"
nrounds	The number of tree
gamma	The gamma parameter that controls the pruning process. The higher gamma the less overfitting.
nthread	The number of CPU.
...	Other parameters see ?xgboost

Examples

set.seed(0)
x=rnorm(1000)
y=exp(x)+rnorm(1000)
result=ANM_XGB(x,y)
if(result$HSIC_xy<result$HSIC_yx){
 print("X->Y")
}else{
 print("Y->X")
}

DMIRLAB-Group/CANM documentation built on May 31, 2019, 12:40 a.m.