Overview

GLASSOO is an R package that estimates a lasso-penalized precision matrix via block-wise coordinate descent – also known as the graphical lasso (glasso) algorithm. This package is similar to CVglasso – but rather than being a wrapper around the glasso package, the code is completely re-written in C++.

A (possibly incomplete) list of functions contained in the package can be found below:

• GLASSO() computes the estimated precision matrix

• plot.GLASSO() produces a heat map or line graph for cross validation errors

See package website or manual.

Installation

# The easiest way to install is from GitHub:
# install.packages("devtools")
devtools::install_github("MGallow/GLASSOO")

If there are any issues/bugs, please let me know: github. You can also contact me via my website. Pull requests are welcome!

Usage

library(GLASSOO)

# generate data from a sparse matrix
# first compute covariance matrix
S = matrix(0.7, nrow = 5, ncol = 5)
for (i in 1:5){
  for (j in 1:5){
    S[i, j] = S[i, j]^abs(i - j)
  }
}

# print oracle precision matrix (shrinkage might be useful)
(Omega = round(qr.solve(S), 3))

##        [,1]   [,2]   [,3]   [,4]   [,5]
## [1,]  1.961 -1.373  0.000  0.000  0.000
## [2,] -1.373  2.922 -1.373  0.000  0.000
## [3,]  0.000 -1.373  2.922 -1.373  0.000
## [4,]  0.000  0.000 -1.373  2.922 -1.373
## [5,]  0.000  0.000  0.000 -1.373  1.961

# generate 100 x 5 matrix with rows drawn from iid N_p(0, S)
set.seed(123)
Z = matrix(rnorm(100*5), nrow = 100, ncol = 5)
out = eigen(S, symmetric = TRUE)
S.sqrt = out$vectors %*% diag(out$values^0.5) %*% t(out$vectors)
X = Z %*% S.sqrt

# calculate sample covariance
sample = (nrow(X) - 1)/nrow(X)*cov(X)

# print sample precision matrix (perhaps a bad estimate)
round(qr.solve(cov(X)), 5)

##          [,1]     [,2]     [,3]     [,4]     [,5]
## [1,]  2.30646 -1.53483  0.21884 -0.08521  0.24066
## [2,] -1.53483  3.24286 -1.66346 -0.14134  0.18760
## [3,]  0.21884 -1.66346  3.16698 -1.23906 -0.10906
## [4,] -0.08521 -0.14134 -1.23906  2.74022 -1.35853
## [5,]  0.24066  0.18760 -0.10906 -1.35853  2.03323

# GLASSO (lam = 0.5)
GLASSO(S = sample, lam = 0.5)

## 
## Call: GLASSO(S = sample, lam = 0.5)
## 
## Iterations:
## [1] 3
## 
## Tuning parameter:
##       log10(lam)  lam
## [1,]      -0.301  0.5
## 
## Log-likelihood: -10.44936
## 
## Omega:
##          [,1]     [,2]     [,3]     [,4]     [,5]
## [1,]  1.34080 -0.00973  0.00000  0.00000  0.00000
## [2,] -0.00973  1.19263 -0.09615  0.00000  0.00000
## [3,]  0.00000 -0.09615  1.21895 -0.11424  0.00000
## [4,]  0.00000  0.00000 -0.11424  1.06968 -0.13534
## [5,]  0.00000  0.00000  0.00000 -0.13534  1.12473

# GLASSO cross validation
(GLASSO = GLASSO(X))

## 
## Call: GLASSO(X = X)
## 
## Iterations:
## [1] 3
## 
## Tuning parameter:
##       log10(lam)    lam
## [1,]      -1.544  0.029
## 
## Log-likelihood: -110.16675
## 
## Omega:
##          [,1]     [,2]     [,3]     [,4]     [,5]
## [1,]  2.13226 -1.24669  0.00000  0.00000  0.18709
## [2,] -1.24669  2.75122 -1.29912 -0.07341  0.00000
## [3,]  0.00000 -1.29912  2.81744 -1.15682 -0.00113
## [4,]  0.00000 -0.07341 -1.15682  2.46461 -1.17086
## [5,]  0.18709  0.00000 -0.00113 -1.17086  1.86326

# produce line graph for CV errors for GLASSO
plot(GLASSO)

# produce CV heat map for GLASSO
plot(GLASSO, type = "heatmap")