| Title: | Estimate Causal Polytree from Data |
| Version: | 0.0.1 |
| Maintainer: | Sourav Chatterjee <souravc@stanford.edu> |
| Description: | Given a data matrix with rows representing data vectors and columns representing variables, produces a directed polytree for the underlying causal structure. Based on the algorithm developed in Chatterjee and Vidyasagar (2022) <doi:10.48550/arXiv.2209.07028>. The method is fully nonparametric, making no use of linearity assumptions, and especially useful when the number of variables is large. |
| License: | MIT + file LICENSE |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.3.1 |
| Imports: | FOCI, igraph |
| NeedsCompilation: | no |
| Packaged: | 2024-03-24 02:47:21 UTC; sourav |
| Author: | Sourav Chatterjee 
[aut, cre] |
| Repository: | CRAN |
| Date/Publication: | 2024-03-25 19:10:12 UTC |
This is the function that computes the skeletion tree from data. The input is a matrix x whose rows are the data vectors. The sample size n is the number of rows. The number of variables p is the number of columns The function outputs the skeleton tree g.
Description
This is the function that computes the skeletion tree from data. The input is a matrix x whose rows are the data vectors. The sample size n is the number of rows. The number of variables p is the number of columns The function outputs the skeleton tree g.
Usage
condeptree(x)
Arguments
x |
The input data matrix. |
Creates an outgoing tree from a given undirected treee.
Description
Creates an outgoing tree from a given undirected treee.
Usage
outgoing(tree, dir_tree = NULL, a = NULL, b = 1)
Arguments
tree |
Input tree, undirected. |
dir_tree |
Directionalities that must be present. |
a |
The node being inspected. |
b |
The neighbor being inspected. |
Causal Polytree Estimation
Description
Estimates directed causal polytree from data, using algorithm developed in Chatterjee and Vidyasagar (2022).
Usage
polytree(x)
Arguments
x |
Data matrix, whose rows are i.i.d. data vectors generated from the model. |
Value
A directed polytree estimated from the input data, as an igraph object.
References
Sourav Chatterjee and Mathukumalli Vidyasagar (2022). Estimating large causal polytrees from small samples. Available at https://arxiv.org/abs/2209.07028
Examples
p <- 10
n <- 200
x <- matrix(nrow = n, ncol = p)
for (i in 1:n) {
x[i,1] = rnorm(1)
for (j in 2:p) {
x[i,j] = (x[i,j-1] + rnorm(1))/sqrt(2)
}
}
p <- polytree(x)
This function computes the xi correlation coefficient.
Description
This function computes the xi correlation coefficient.
Usage
xicorln(xvec, yvec)
Arguments
xvec |
The vector of x values. |
yvec |
The vector of y values. |