MGL: Module network inference
In MGL: Module Graphical Lasso
Description
Usage
Arguments
Value
Examples
Description
Takes a high-dimensional data matrix, initial values of the module latent variables, and a penalty parameter,
and returns the final assignment of the data points to the modules, the values of the module latent variables,
and the conditional dependency network among the module latent variables.
Usage
1
Arguments
data
An nxp matrix which contains n samples from p variables, where typically p>>n
L
An nxk matrix which contains the initial latent variable values, a column for each module
lambda
A penalty parameter controlling the sparsity of the conditional dependency network among the modules
printoutput
1 if the user wants the output from each iteration to be displayed, 0 for silent run
maxiter
Maximum number of iterations to be performed
threshold
Threshold for convergence
Value
L
An nxk matrix which contains the final latent variable values, a column for each module
theta
A kxk symmetric positive-semidefinite matrix respresenting the conditional dependency network among the modules
Z
A p-vector containing values between 1 to k, representing the assignment of the p variables to k modules
Examples
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
## Not run:
library(MGL)
n = 20 #sample size
p = 100 #variable size
k = 5 #module size
lambda = .01 #penalty parameter to induce sparsity
data = matrix(rnorm(n*p), ncol=p)
# to start with initial random module latent variables
L = matrix(rnorm(n*k), ncol=k)
MGL(data, L, lambda)
# to start with k-means cluster centroids as module latent variables
L = t(kmeans(t(data), k)$centers)
MGL(data, L, lambda)
## End(Not run)
Example output
$L
[,1] [,2] [,3] [,4] [,5]
[1,] 0.065985900 -0.10994705 0.18968118 -0.002444374 0.152631596
[2,] 0.519741112 0.15428774 -0.47236383 0.444131722 -0.004330232
[3,] 0.561965748 -0.34571267 -0.12982628 -0.127936276 0.011401763
[4,] -0.060662924 0.20697618 -0.18833723 -0.240796944 0.258187340
[5,] -0.448130135 -0.10426775 0.03327597 -0.157302242 0.056876829
[6,] 0.069914897 0.12830753 0.13375113 0.041594802 -0.116276989
[7,] -0.317607942 -0.15040364 0.35563519 -0.304017734 0.103715765
[8,] 0.027632474 0.54809314 -0.09428134 -0.624184172 -0.019306193
[9,] -0.042168542 0.03586131 -0.18271092 -0.005992714 -0.012063559
[10,] -0.203113400 0.25728039 0.42419598 -0.584072619 -0.085267086
[11,] -0.196574905 -0.26536624 0.18305737 0.121533247 0.058019434
[12,] 0.009327154 0.05032727 -0.28139280 -0.077235267 0.022258017
[13,] -0.392590581 0.08938219 0.34488776 0.223235553 0.029488306
[14,] -0.160175231 -0.40865862 0.39857834 -0.250538673 0.046423334
[15,] -0.037732630 -0.45606974 0.36803757 -0.091135731 0.038712549
[16,] 0.126513076 0.33975780 -0.48012763 -0.040205396 0.061406681
[17,] -0.193500111 -0.17666197 0.46659282 -0.362067748 -0.023941664
[18,] -0.092076686 -0.10735821 0.32574788 0.254915978 -0.017420289
[19,] -0.003051776 -0.44362021 0.11779685 0.081029296 0.007277365
[20,] 0.300855738 0.02220916 -0.17154681 -0.042697782 0.026145286
$theta
[,1] [,2] [,3] [,4] [,5]
[1,] 15.990555 0.000000 6.259901 -1.020372 0.0000
[2,] 0.000000 13.402345 3.948230 1.609429 0.0000
[3,] 6.259900 3.948230 13.738734 1.412712 0.0000
[4,] -1.020368 1.609433 1.412715 13.043791 0.0000
[5,] 0.000000 0.000000 0.000000 0.000000 60.9878
$Z
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14]
[1,] 5 1 5 5 1 2 2 4 5 3 1 1 3 4
[,15] [,16] [,17] [,18] [,19] [,20] [,21] [,22] [,23] [,24] [,25] [,26]
[1,] 5 1 4 4 5 5 2 2 5 3 2 3
[,27] [,28] [,29] [,30] [,31] [,32] [,33] [,34] [,35] [,36] [,37] [,38]
[1,] 2 5 2 3 3 3 2 3 3 5 5 2
[,39] [,40] [,41] [,42] [,43] [,44] [,45] [,46] [,47] [,48] [,49] [,50]
[1,] 2 2 4 1 2 4 1 3 5 3 1 3
[,51] [,52] [,53] [,54] [,55] [,56] [,57] [,58] [,59] [,60] [,61] [,62]
[1,] 4 1 5 1 1 1 4 3 3 4 1 5
[,63] [,64] [,65] [,66] [,67] [,68] [,69] [,70] [,71] [,72] [,73] [,74]
[1,] 2 4 1 2 1 4 5 1 2 1 3 3
[,75] [,76] [,77] [,78] [,79] [,80] [,81] [,82] [,83] [,84] [,85] [,86]
[1,] 5 5 2 2 3 4 2 5 5 4 3 5
[,87] [,88] [,89] [,90] [,91] [,92] [,93] [,94] [,95] [,96] [,97] [,98]
[1,] 1 5 3 3 3 2 3 2 3 4 4 3
[,99] [,100]
[1,] 4 4
$L
[,1] [,2] [,3] [,4] [,5]
[1,] 0.25494475 -0.11046021 0.18122553 0.063439627 0.08549681
[2,] -0.19257443 0.46946620 0.16831998 0.028714713 0.12930545
[3,] -0.26764080 -0.25597938 0.48490157 -0.387156439 0.52394112
[4,] -0.21562794 -0.20935185 0.45172629 -0.199451849 0.50720745
[5,] 0.08168064 0.10536662 0.26704888 -0.343336897 -0.53164601
[6,] 0.32534551 -0.54680865 -0.26359608 0.189599088 0.21295715
[7,] -0.27880680 0.12299151 -0.14229353 0.259471868 -0.04143980
[8,] -0.54438595 -0.04978158 0.16570807 0.318817730 -0.03486373
[9,] -0.72372702 0.13327832 -0.19926064 0.385529497 0.11168252
[10,] 0.68050349 -0.79943317 -0.26310827 -0.095253809 0.13789118
[11,] 0.53698069 -0.10606117 -0.64352361 -0.169835353 0.30958454
[12,] -0.24928764 0.14587913 -0.07237650 0.004545797 -0.09847205
[13,] 0.17146610 0.59620231 -0.44712009 -0.096395987 0.31937510
[14,] 0.17081777 -0.25795432 -0.63495135 0.213543817 0.14258392
[15,] -0.10494809 -0.17161716 -0.09875534 -0.101962115 0.46048262
[16,] -0.09533873 -0.27637766 0.26219202 -0.186505099 0.26740596
[17,] 0.24394967 -0.50267508 -0.42369468 0.414532289 -0.18261635
[18,] 0.18900482 0.14620906 -0.01601017 0.142261361 -0.08890176
[19,] 0.22237079 -0.30090459 0.47933560 -0.481916037 -0.07291382
[20,] -0.18325738 0.04565754 0.10445782 0.285746573 -0.17421670
$theta
[,1] [,2] [,3] [,4] [,5]
[1,] 9.915211 2.9750608 3.407036 3.161608 0.0000000
[2,] 2.975057 9.3792695 0.000000 0.000000 0.1906305
[3,] 3.407041 0.0000000 9.880113 4.888929 0.0000000
[4,] 3.161609 0.0000000 4.888928 15.521753 1.2821712
[5,] 0.000000 0.1906306 0.000000 1.282171 12.8016957
$Z
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14]
[1,] 3 3 3 4 4 1 1 2 5 1 5 3 5 2
[,15] [,16] [,17] [,18] [,19] [,20] [,21] [,22] [,23] [,24] [,25] [,26]
[1,] 4 5 2 3 5 1 4 4 1 1 5 5
[,27] [,28] [,29] [,30] [,31] [,32] [,33] [,34] [,35] [,36] [,37] [,38]
[1,] 5 5 2 1 1 1 4 4 1 4 5 3
[,39] [,40] [,41] [,42] [,43] [,44] [,45] [,46] [,47] [,48] [,49] [,50]
[1,] 4 2 1 3 2 2 3 4 5 4 5 4
[,51] [,52] [,53] [,54] [,55] [,56] [,57] [,58] [,59] [,60] [,61] [,62]
[1,] 3 3 1 4 3 5 2 1 5 2 3 3
[,63] [,64] [,65] [,66] [,67] [,68] [,69] [,70] [,71] [,72] [,73] [,74]
[1,] 4 2 5 5 3 1 2 1 5 4 4 4
[,75] [,76] [,77] [,78] [,79] [,80] [,81] [,82] [,83] [,84] [,85] [,86]
[1,] 5 5 4 3 5 4 4 2 2 1 1 4
[,87] [,88] [,89] [,90] [,91] [,92] [,93] [,94] [,95] [,96] [,97] [,98]
[1,] 4 3 1 4 1 3 1 4 5 5 1 1
[,99] [,100]
[1,] 2 2
MGL documentation built on May 2, 2019, 1:08 p.m.
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Description Usage Arguments Value Examples
Description
Takes a high-dimensional data matrix, initial values of the module latent variables, and a penalty parameter, and returns the final assignment of the data points to the modules, the values of the module latent variables, and the conditional dependency network among the module latent variables.
Usage
1 |
Arguments
data |
An nxp matrix which contains n samples from p variables, where typically p>>n |
L |
An nxk matrix which contains the initial latent variable values, a column for each module |
lambda |
A penalty parameter controlling the sparsity of the conditional dependency network among the modules |
printoutput |
1 if the user wants the output from each iteration to be displayed, 0 for silent run |
maxiter |
Maximum number of iterations to be performed |
threshold |
Threshold for convergence |
Value
L |
An nxk matrix which contains the final latent variable values, a column for each module |
theta |
A kxk symmetric positive-semidefinite matrix respresenting the conditional dependency network among the modules |
Z |
A p-vector containing values between 1 to k, representing the assignment of the p variables to k modules |
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | ## Not run:
library(MGL)
n = 20 #sample size
p = 100 #variable size
k = 5 #module size
lambda = .01 #penalty parameter to induce sparsity
data = matrix(rnorm(n*p), ncol=p)
# to start with initial random module latent variables
L = matrix(rnorm(n*k), ncol=k)
MGL(data, L, lambda)
# to start with k-means cluster centroids as module latent variables
L = t(kmeans(t(data), k)$centers)
MGL(data, L, lambda)
## End(Not run)
|
Example output
$L
[,1] [,2] [,3] [,4] [,5]
[1,] 0.065985900 -0.10994705 0.18968118 -0.002444374 0.152631596
[2,] 0.519741112 0.15428774 -0.47236383 0.444131722 -0.004330232
[3,] 0.561965748 -0.34571267 -0.12982628 -0.127936276 0.011401763
[4,] -0.060662924 0.20697618 -0.18833723 -0.240796944 0.258187340
[5,] -0.448130135 -0.10426775 0.03327597 -0.157302242 0.056876829
[6,] 0.069914897 0.12830753 0.13375113 0.041594802 -0.116276989
[7,] -0.317607942 -0.15040364 0.35563519 -0.304017734 0.103715765
[8,] 0.027632474 0.54809314 -0.09428134 -0.624184172 -0.019306193
[9,] -0.042168542 0.03586131 -0.18271092 -0.005992714 -0.012063559
[10,] -0.203113400 0.25728039 0.42419598 -0.584072619 -0.085267086
[11,] -0.196574905 -0.26536624 0.18305737 0.121533247 0.058019434
[12,] 0.009327154 0.05032727 -0.28139280 -0.077235267 0.022258017
[13,] -0.392590581 0.08938219 0.34488776 0.223235553 0.029488306
[14,] -0.160175231 -0.40865862 0.39857834 -0.250538673 0.046423334
[15,] -0.037732630 -0.45606974 0.36803757 -0.091135731 0.038712549
[16,] 0.126513076 0.33975780 -0.48012763 -0.040205396 0.061406681
[17,] -0.193500111 -0.17666197 0.46659282 -0.362067748 -0.023941664
[18,] -0.092076686 -0.10735821 0.32574788 0.254915978 -0.017420289
[19,] -0.003051776 -0.44362021 0.11779685 0.081029296 0.007277365
[20,] 0.300855738 0.02220916 -0.17154681 -0.042697782 0.026145286
$theta
[,1] [,2] [,3] [,4] [,5]
[1,] 15.990555 0.000000 6.259901 -1.020372 0.0000
[2,] 0.000000 13.402345 3.948230 1.609429 0.0000
[3,] 6.259900 3.948230 13.738734 1.412712 0.0000
[4,] -1.020368 1.609433 1.412715 13.043791 0.0000
[5,] 0.000000 0.000000 0.000000 0.000000 60.9878
$Z
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14]
[1,] 5 1 5 5 1 2 2 4 5 3 1 1 3 4
[,15] [,16] [,17] [,18] [,19] [,20] [,21] [,22] [,23] [,24] [,25] [,26]
[1,] 5 1 4 4 5 5 2 2 5 3 2 3
[,27] [,28] [,29] [,30] [,31] [,32] [,33] [,34] [,35] [,36] [,37] [,38]
[1,] 2 5 2 3 3 3 2 3 3 5 5 2
[,39] [,40] [,41] [,42] [,43] [,44] [,45] [,46] [,47] [,48] [,49] [,50]
[1,] 2 2 4 1 2 4 1 3 5 3 1 3
[,51] [,52] [,53] [,54] [,55] [,56] [,57] [,58] [,59] [,60] [,61] [,62]
[1,] 4 1 5 1 1 1 4 3 3 4 1 5
[,63] [,64] [,65] [,66] [,67] [,68] [,69] [,70] [,71] [,72] [,73] [,74]
[1,] 2 4 1 2 1 4 5 1 2 1 3 3
[,75] [,76] [,77] [,78] [,79] [,80] [,81] [,82] [,83] [,84] [,85] [,86]
[1,] 5 5 2 2 3 4 2 5 5 4 3 5
[,87] [,88] [,89] [,90] [,91] [,92] [,93] [,94] [,95] [,96] [,97] [,98]
[1,] 1 5 3 3 3 2 3 2 3 4 4 3
[,99] [,100]
[1,] 4 4
$L
[,1] [,2] [,3] [,4] [,5]
[1,] 0.25494475 -0.11046021 0.18122553 0.063439627 0.08549681
[2,] -0.19257443 0.46946620 0.16831998 0.028714713 0.12930545
[3,] -0.26764080 -0.25597938 0.48490157 -0.387156439 0.52394112
[4,] -0.21562794 -0.20935185 0.45172629 -0.199451849 0.50720745
[5,] 0.08168064 0.10536662 0.26704888 -0.343336897 -0.53164601
[6,] 0.32534551 -0.54680865 -0.26359608 0.189599088 0.21295715
[7,] -0.27880680 0.12299151 -0.14229353 0.259471868 -0.04143980
[8,] -0.54438595 -0.04978158 0.16570807 0.318817730 -0.03486373
[9,] -0.72372702 0.13327832 -0.19926064 0.385529497 0.11168252
[10,] 0.68050349 -0.79943317 -0.26310827 -0.095253809 0.13789118
[11,] 0.53698069 -0.10606117 -0.64352361 -0.169835353 0.30958454
[12,] -0.24928764 0.14587913 -0.07237650 0.004545797 -0.09847205
[13,] 0.17146610 0.59620231 -0.44712009 -0.096395987 0.31937510
[14,] 0.17081777 -0.25795432 -0.63495135 0.213543817 0.14258392
[15,] -0.10494809 -0.17161716 -0.09875534 -0.101962115 0.46048262
[16,] -0.09533873 -0.27637766 0.26219202 -0.186505099 0.26740596
[17,] 0.24394967 -0.50267508 -0.42369468 0.414532289 -0.18261635
[18,] 0.18900482 0.14620906 -0.01601017 0.142261361 -0.08890176
[19,] 0.22237079 -0.30090459 0.47933560 -0.481916037 -0.07291382
[20,] -0.18325738 0.04565754 0.10445782 0.285746573 -0.17421670
$theta
[,1] [,2] [,3] [,4] [,5]
[1,] 9.915211 2.9750608 3.407036 3.161608 0.0000000
[2,] 2.975057 9.3792695 0.000000 0.000000 0.1906305
[3,] 3.407041 0.0000000 9.880113 4.888929 0.0000000
[4,] 3.161609 0.0000000 4.888928 15.521753 1.2821712
[5,] 0.000000 0.1906306 0.000000 1.282171 12.8016957
$Z
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14]
[1,] 3 3 3 4 4 1 1 2 5 1 5 3 5 2
[,15] [,16] [,17] [,18] [,19] [,20] [,21] [,22] [,23] [,24] [,25] [,26]
[1,] 4 5 2 3 5 1 4 4 1 1 5 5
[,27] [,28] [,29] [,30] [,31] [,32] [,33] [,34] [,35] [,36] [,37] [,38]
[1,] 5 5 2 1 1 1 4 4 1 4 5 3
[,39] [,40] [,41] [,42] [,43] [,44] [,45] [,46] [,47] [,48] [,49] [,50]
[1,] 4 2 1 3 2 2 3 4 5 4 5 4
[,51] [,52] [,53] [,54] [,55] [,56] [,57] [,58] [,59] [,60] [,61] [,62]
[1,] 3 3 1 4 3 5 2 1 5 2 3 3
[,63] [,64] [,65] [,66] [,67] [,68] [,69] [,70] [,71] [,72] [,73] [,74]
[1,] 4 2 5 5 3 1 2 1 5 4 4 4
[,75] [,76] [,77] [,78] [,79] [,80] [,81] [,82] [,83] [,84] [,85] [,86]
[1,] 5 5 4 3 5 4 4 2 2 1 1 4
[,87] [,88] [,89] [,90] [,91] [,92] [,93] [,94] [,95] [,96] [,97] [,98]
[1,] 4 3 1 4 1 3 1 4 5 5 1 1
[,99] [,100]
[1,] 2 2
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