Description Usage Arguments Value References Examples
View source: R/test_indep_com.R
Implements the pseudo pseudolikelihood ratio test described in Section 3 of Gao et. al. (2019) "Testing for Association in Multi-View Network Data" for testing for dependence between communities in two network data views. Fits stochastic block models in each view.
1 2 3 4 5 6 7 8 9 |
X |
Multi-view data with two views; a list of two n x n adjacency matrices. |
K1 |
An optional argument containing the number of communities in View 1. If left out, then the number of communities is chosen with the method of Le and Levina (2015). |
K2 |
An optional argument containing the number of communities in View 2. If left out, then the number of communities is chosen with the method of Le and Levina (2015). |
nperm |
An integer specifying the number of permutations to use for the permutation procedure. The default number is 200. |
step |
A numeric value containing the fixed step size to be used in the optimization algorithm for estimating Pi. The default step size is 0.001. |
maxiter |
A numeric value containing the maximum number of iterations to run in the optimization algorithm. The default maximum is 1000. |
parallel |
An optional argument; if true, do parallel computing using the doParallel package |
A list containing the following output components:
K1 |
The number of communities in view 1 |
K2 |
The number of communities in view 2 |
Pi.est |
The estimated Pi matrix |
P2LRstat |
The pseudo likelihood ratio test statistic |
pval |
The p-value |
modelfit1 |
The parameter estimates and community assignment estimates from View 1. |
modelfit2 |
The parameter estimates and community assignment estimates from View 2. |
Amini, A. A., Chen, A., Bickel, P. J., & Levina, E. (2013). Pseudo-likelihood methods for community detection in large sparse networks. The Annals of Statistics, 41(4), 2097-2122.
Gao, L.L., Witten, D., Bien, J. Testing for Association in Multi-View Network Data, preprint.
Le, C. M., & Levina, E. (2015). Estimating the number of communities in networks by spectral methods. arXiv preprint arXiv:1507.00827.
1 2 3 4 5 6 7 8 9 10 11 12 13 | set.seed(1)
n <- 50
Pi <- diag(c(0.5, 0.5))
theta1 <- rbind(c(0.5, 0.1), c(0.1, 0.5))
theta2 <- cbind(c(0.1, 0.5), c(0.5, 0.1))
# 50 draws from a multi-view SBM with perfectly dependent communities
dat <- mv_sbm_gen(n, Pi, theta1, theta2)
# Test H0: communities are independent
# Data was generated under the alternative hypothesis
results <- test_indep_com(dat$data, nperm=25)
results$pval
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