fhc: Fast Hill-Climbing

In DMIRLAB-Group/SELF: A Structural Equation Embedded Likelihood Framework for Causal Discovery

Description

The function for the causal structure learning.

Usage

fhc(D, G = NULL, min_increase = 0.01, score_type = "bic", file = "",
  verbose = TRUE, save_model = FALSE, bw = "nrd0", booster = "gbtree",
  gamma = 10, nrounds = 30, ...)

Arguments

D	Input Data.
G	An initial graph for hill climbing. Default: empty graph.
min_increase	Minimum score increase for faster convergence.
score_type	You can choose "bic","log","aic" score to learn the causal struture. Default: bic
file	Specifies the output folder and its path to save the model at each iteration.
verbose	Show the progress bar for each iteration.
save_model	Save the meta data during the iteration so that you can easily restore progress and evaluate the model during iteration.
bw	the smoothing bandwidth which is the parameter of the function stats::density(Kernel stats::density Estimation)
booster	Choose the regression method, it could be "lm", "gbtree" and "gblinear". The "lm" and "gblinear" is the linear regression methods and "gbtree" is the nonlinear regression method. Default: gbtree
gamma	The parameter in xgboost: minimum loss reduction required to make a further partition on a leaf node of the tree. the larger, the more conservative the algorithm will be.
nrounds	the maximum number of trees for xgboost.Default:30.
...	other parameters for xgboost.see also: help(xgboost)

Value

The adjacency matrix of the casual structure.

Examples

## Not run: 
#x->y->z
set.seed(0)
x=rnorm(4000)
y=x^2+runif(4000,-1,1)*0.1
z=y^2+runif(4000,-1,1)*0.1
data=data.frame(x,y,z)
fhc(data,gamma=10,booster = "gbtree")

#x->y->z linear data
set.seed(0)
x=rnorm(4000)
y=3*x+runif(4000,-1,1)*0.1
z=3*y+runif(4000,-1,1)*0.1
data=data.frame(x,y,z)
fhc(data,booster = "lm")

#randomGraph with linear data

set.seed(0)
G=randomGraph(dim=10,indegree=1.5)
data=synthetic_data_linear(G=G,sample_num=4000)
fitG=fhc(data,booster = "lm")
indicators(fitG,G)

## End(Not run)

DMIRLAB-Group/SELF documentation built on May 28, 2019, 12:18 p.m.