Skip to content

RoheLab/vsp

main
Switch branches/tags
Code

Latest commit

 

Git stats

Files

Permalink
Failed to load latest commit information.
Type
Name
Latest commit message
Commit time
 
 
R
 
 
 
 
man
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

vsp

Codecov test coverage R-CMD-check CRAN status

The goal of vsp is to enable fast, spectral estimation of latent factors in random dot product graphs. Under mild assumptions, the vsp estimator is consistent for (degree-corrected) stochastic blockmodels, (degree-corrected) mixed-membership stochastic blockmodels, and degree-corrected overlapping stochastic blockmodels.

More generally, the vsp estimator is consistent for random dot product graphs that can be written in the form

E(A) = Z B Y^T

where Z and Y satisfy the varimax assumptions of [1]. vsp works on directed and undirected graphs, and on weighted and unweighted graphs. Note that vsp is a semi-parametric estimator.

Installation

You can install the released version of vsp from CRAN with

install.packages("vsp")

You can install the development version of vsp with:

install.packages("devtools")
devtools::install_github("RoheLab/vsp")

Example

Obtaining estimates from vsp is straightforward. We recommend representing networks as igraph objects or sparse adjacency matrices using the Matrix package. Once you have your network in one of these formats, you can get estimates by calling the vsp() function. The result is a vsp_fa S3 object.

Here we demonstrate vsp usage on an igraph object, using the enron network from igraphdata package to demonstrate this functionality. First we peak at the graph:

library(igraph)
data(enron, package = "igraphdata")

image(sign(get.adjacency(enron, sparse = FALSE)))

Now we estimate:

library(vsp)

fa <- vsp(enron, rank = 30)
fa
#> Vintage Sparse PCA Factor Analysis
#> 
#> Rows (n):   184
#> Cols (d):   184
#> Factors (rank): 30
#> Lambda[rank]:   0.2077
#> Components
#> 
#> Z: 184 x 30 [dgeMatrix] 
#> B: 30 x 30 [dgeMatrix] 
#> Y: 184 x 30 [dgeMatrix] 
#> u: 184 x 30 [matrix] 
#> d: 30      [numeric] 
#> v: 184 x 30 [matrix]
get_varimax_z(fa)
#> # A tibble: 184 × 31
#>    id         z01      z02      z03      z04      z05      z06      z07      z08
#>    <chr>    <dbl>    <dbl>    <dbl>    <dbl>    <dbl>    <dbl>    <dbl>    <dbl>
#>  1 row0…  2.42e-4 -0.00245 -2.99e-2  3.37e-4  9.96e-5 -0.0114  -0.00849  0.502  
#>  2 row0… -2.52e-3  0.00135  6.70e-4 -1.63e-1 -1.47e-2  0.0471   0.190    0.00181
#>  3 row0…  2.98e-4 -0.100    1.17e-4 -3.62e-3 -2.06e-2  0.187   -0.158    0.00303
#>  4 row0… -7.75e-5 -0.0183   1.17e-4  5.42e-2 -5.58e-3  0.00165 -0.0367  -0.00106
#>  5 row0… -2.31e-3  0.00150  2.57e-1 -1.42e-2 -4.38e-2  0.00629  1.18    -0.0179 
#>  6 row0… -3.46e-2 -0.0527  -2.61e-2 -1.26e-2 -1.83e-2  0.0282   0.408   -0.0286 
#>  7 row0… -1.08e-3 -0.327   -6.01e-1 -6.98e-2 -9.85e-2 -0.0709   0.509    0.0511 
#>  8 row0…  1.58e-2 -0.0518  -1.34e-2 -1.03e-2 -4.12e-3 -0.0139   0.225   -0.0244 
#>  9 row0…  2.22e-3  0.0752   3.30e-2 -6.50e-4 -5.00e-1 -0.0278  -0.0740  -0.00556
#> 10 row0…  7.13e-4 -0.0119   1.95e-2 -5.06e-3 -7.08e-3  0.00341 -0.00369 13.4    
#> # … with 174 more rows, and 22 more variables: z09 <dbl>, z10 <dbl>, z11 <dbl>,
#> #   z12 <dbl>, z13 <dbl>, z14 <dbl>, z15 <dbl>, z16 <dbl>, z17 <dbl>,
#> #   z18 <dbl>, z19 <dbl>, z20 <dbl>, z21 <dbl>, z22 <dbl>, z23 <dbl>,
#> #   z24 <dbl>, z25 <dbl>, z26 <dbl>, z27 <dbl>, z28 <dbl>, z29 <dbl>, z30 <dbl>

To visualize a screeplot of the singular value, use:

screeplot(fa)

At the moment, we also enjoy using pairs plots of the factors as a diagnostic measure:

plot_varimax_z_pairs(fa, 1:5)

plot_varimax_y_pairs(fa, 1:5)

Similarly, an IPR pairs plot can be a good way to check for singular vector localization (and thus overfitting!).

plot_ipr_pairs(fa)

plot_mixing_matrix(fa)

References

  1. Rohe, K. & Zeng, M. Vintage Factor Analysis with Varimax Performs Statistical Inference. 2022+. https://arxiv.org/abs/2004.05387.

Code to reproduce the results from the paper is available here.

About

Vintage Sparse PCA for Semi-Parametric Network Analysis

Resources

License

Unknown, MIT licenses found

Licenses found

Unknown
LICENSE
MIT
LICENSE.md

Stars

Watchers

Forks

Languages