fhc {SELF}

Fast Hill-Climbing

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)