| Type: | Package |
| Title: | A Structural Equation Embedded Likelihood Framework for Causal Discovery |
| Version: | 0.1.1 |
| Author: | Ruichu Cai [ths, aut], Jie Qiao [aut, cre], Zhenjie Zhang [ths, aut], Zhifeng Hao [ths, aut] |
| Maintainer: | Jie Qiao <qiaojie2004@vip.qq.com> |
| Description: | Provides the SELF criteria to learn causal structure. Please cite "Ruichu Cai, Jie Qiao, Zhenjie Zhang, Zhifeng Hao. SELF: Structural Equational Embedded Likelihood Framework for Causal Discovery. AAAI. 2018." |
| License: | GPL-2 |
| LazyData: | true |
| Imports: | data.table (≥ 1.10.4), xgboost (≥ 0.6-4), Rcpp (≥ 0.12.10), CompareCausalNetworks (≥ 0.1.0), bnlearn (≥ 4.1.1) |
| LinkingTo: | Rcpp |
| RoxygenNote: | 6.0.1 |
| Encoding: | UTF-8 |
| NeedsCompilation: | yes |
| Packaged: | 2017-11-22 11:46:00 UTC; qj |
| Repository: | CRAN |
| Date/Publication: | 2017-11-22 13:24:22 UTC |
SELF: A Structural Equation Embedded Likelihood Framework for Causal Discovery
Description
Provides the SELF criteria to learn causal structure. Please cite "Ruichu Cai, Jie Qiao, Zhenjie Zhang, Zhifeng Hao. SELF: Structural Equational Embedded Likelihood Framework for Causal Discovery. AAAI. 2018."
Author(s)
Maintainer: Jie Qiao qiaojie2004@vip.qq.com
Authors:
Ruichu Cai cairuichu@gmail.com [thesis advisor]
Zhenjie Zhang zhenjie@adsc.com.sg [thesis advisor]
Zhifeng Hao zfhao@gdut.edu.cn [thesis advisor]
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)
Calculate the f1,precision,recall score of the graph
Description
Calculate the f1,precision,recall score of the graph
Usage
indicators(pred, real)
Arguments
pred |
Predicted graph |
real |
Real graph |
Value
f1,precision,recall score.
Examples
pred<-matrix(c(0,0,0,0,1,0,1,1,0),nrow=3,ncol=3)
real<-matrix(c(0,0,0,0,1,0,1,0,0),nrow=3,ncol=3)
indicators(pred,real)
mmpc algorithm with additive noise model
Description
The nonlinear data comparison algorithm. We use the mmpc algorithm to learn a causal skeleton and use ANM to recognize the direction
Usage
mmpcAnm(data)
Arguments
data |
The data |
Generate a random graph
Description
Generate a random graph based on the given dimension size and average indegree
Usage
randomGraph(dim, indegree, maxite = 10000)
Arguments
dim |
The dimension of the random graph |
indegree |
The average indegree of random graph for each nodes |
maxite |
The maximum iterations to find the random graph |
Value
Return a random graph
Examples
randomGraph(dim=10,indegree=1)
synthetic linear data base on the graph
Description
Synthetic linear data base on the graph. The noises are sampled from the super-gaussian distribution. The coefficients are sample from U(-1,-0.5),U(0.5,1)
Usage
synthetic_data_linear(G, sample_num, ratio = 1, return_noise = FALSE)
Arguments
G |
An adjacency matrix. |
sample_num |
The number of samples |
ratio |
The noise ratio It will grow or shrink the value of the noise |
return_noise |
Whether return the noise of each nodes for further analysis. |
Value
Return a synthetic data
Examples
G<-matrix(c(0,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0),nrow = 4,ncol = 4)
data=synthetic_data_linear(G,100)
synthetic nonlinear data base on the graph
Description
synthetic nonlinear data base on the graph. The data generation mechanism is y=scale(a1b1x^2+a2b2x^3+a3b3x^4+a4b4sin(x)+a5b5sin(x^2)).
Usage
synthetic_data_nonlinear(G, sample_num, ratio = 1, return_noise = FALSE)
Arguments
G |
An adjacency matrix. |
sample_num |
The number of samples |
ratio |
The noise ratio. It will grow or shrink the value of the noise. |
return_noise |
Whether return the noise of each nodes for further analysis. |
Value
Return a synthetic data
Examples
G<-matrix(c(0,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0),nrow = 4,ncol = 4)
data=synthetic_data_nonlinear(G,100)