Prune square matrix to those variables having nonzero entries

Convenience function that prunes a square matrix to those variables (features) having nonzero row (column) entries (i.e., to features implied in graphical connections).

pruneMatrix(M)

Arguments

M

(Possibly sparsified) square matrix.

Value

A pruned matrix.

Author

Carel F.W. Peeters <carel.peeters@wur.nl>

Examples

## Obtain some (high-dimensional) data
p = 25
n = 10
set.seed(333)
X = matrix(rnorm(n*p), nrow = n, ncol = p)
colnames(X)[1:25] = letters[1:25]

## Obtain regularized precision under optimal penalty
OPT <- optPenalty.LOOCV(X, lambdaMin = .5, lambdaMax = 30, step = 100)
#> Perform input checks... 
#> Calculating cross-validated negative log-likelihoods...
#> lambda = 0.5 done 
#> lambda = 0.521112064796442 done 
#> lambda = 0.543115568152822 done 
#> lambda = 0.56604815028642 done 
#> lambda = 0.589949040739926 done 
#> lambda = 0.614859125489326 done 
#> lambda = 0.640821016885354 done 
#> lambda = 0.667879126548165 done 
#> lambda = 0.696079741339917 done 
#> lambda = 0.725471102545235 done 
#> lambda = 0.756103488394997 done 
#> lambda = 0.788029300074619 done 
#> lambda = 0.821303151363959 done 
#> lambda = 0.855981962062195 done 
#> lambda = 0.89212505535748 done 
#> lambda = 0.929794259307953 done 
#> lambda = 0.969054012607691 done 
#> lambda = 1.00997147481854 done 
#> lambda = 1.0526166412564 done 
#> lambda = 1.09706246272843 done 
#> lambda = 1.14338497032617 done 
#> lambda = 1.19166340548777 done 
#> lambda = 1.24198035555219 done 
#> lambda = 1.29442189503684 done 
#> lambda = 1.34907773288074 done 
#> lambda = 1.40604136590477 done 
#> lambda = 1.46541023875169 done 
#> lambda = 1.52728591057948 done 
#> lambda = 1.59177422879317 done 
#> lambda = 1.65898551011235 done 
#> lambda = 1.72903472928405 done 
#> lambda = 1.80204171576394 done 
#> lambda = 1.87813135870213 done 
#> lambda = 1.95743382058443 done 
#> lambda = 2.04008475989428 done 
#> lambda = 2.12622556317653 done 
#> lambda = 2.21600358689979 done 
#> lambda = 2.30957240953135 done 
#> lambda = 2.40709209425555 done 
#> lambda = 2.5087294627854 done 
#> lambda = 2.61465838073554 done 
#> lambda = 2.72506005504483 done 
#> lambda = 2.84012334395744 done 
#> lambda = 2.96004508009247 done 
#> lambda = 3.08503040715507 done 
#> lambda = 3.21529313086478 done 
#> lambda = 3.35105608470152 done 
#> lambda = 3.49255151109498 done 
#> lambda = 3.64002145870927 done 
#> lambda = 3.79371819650269 done 
#> lambda = 3.9539046452707 done 
#> lambda = 4.12085482741052 done 
#> lambda = 4.29485433567656 done 
#> lambda = 4.47620082172873 done 
#> lambda = 4.66520450530918 done 
#> lambda = 4.86218870491866 done 
#> lambda = 5.0674903909002 done 
#> lambda = 5.28146076187626 done 
#> lambda = 5.50446584552545 done 
#> lambda = 5.73688712472652 done 
#> lambda = 5.97912219014072 done 
#> lambda = 6.23158542034891 done 
#> lambda = 6.49470869070685 done 
#> lambda = 6.76894211213128 done 
#> lambda = 7.05475480108065 done 
#> lambda = 7.3526356820475 done 
#> lambda = 7.66309432393553 done 
#> lambda = 7.98666181175188 done 
#> lambda = 8.32389165510583 done 
#> lambda = 8.67536073506814 done 
#> lambda = 9.04167029101067 done 
#> lambda = 9.42344694911443 done 
#> lambda = 9.8213437943055 done 
#> lambda = 10.2360414874525 done 
#> lambda = 10.6682494297369 done 
#> lambda = 11.1187069761873 done 
#> lambda = 11.5881847004551 done 
#> lambda = 12.0774857129934 done 
#> lambda = 12.587447034895 done 
#> lambda = 13.11894102974 done 
#> lambda = 13.6728768959011 done 
#> lambda = 14.2502022218612 done 
#> lambda = 14.8519046072019 done 
#> lambda = 15.4790133520375 done 
#> lambda = 16.1326012177839 done 
#> lambda = 16.813786262274 done 
#> lambda = 17.5237337523593 done 
#> lambda = 18.2636581572701 done 
#> lambda = 19.0348252261428 done 
#> lambda = 19.8385541532693 done 
#> lambda = 20.6762198347724 done 
#> lambda = 21.5492552205668 done 
#> lambda = 22.4591537656302 done 
#> lambda = 23.4074719847766 done 
#> lambda = 24.3958321153036 done 
#> lambda = 25.4259248920664 done 
#> lambda = 26.499512439728 done 
#> lambda = 27.6184312871313 done 
#> lambda = 28.7845955089513 done 
#> lambda = 30 done 


## Determine support regularized standardized precision under optimal penalty
PC0 <- sparsify(symm(OPT$optPrec), threshold = "localFDR")$sparseParCor
#> Step 1... determine cutoff point
#> Step 2... estimate parameters of null distribution and eta0
#> Step 3... compute p-values and estimate empirical PDF/CDF
#> Step 4... compute q-values and local fdr
#> Step 5... prepare for plotting

#> 
#> - Retained elements:  11 
#> - Corresponding to 3.67 % of possible edges 
#>  

## Prune sparsified partial correlation matrix
PC0P <- pruneMatrix(PC0)