sparseDecom2boot.RdDecomposes two matrices into paired sparse eigenevectors to maximize canonical correlation.
sparseDecom2boot( inmatrix, inmask = c(NULL, NULL), sparseness = c(0.01, 0.01), nvecs = 50, its = 5, cthresh = c(0, 0), statdir = NA, perms = 0, uselong = 0, z = 0, smooth = 0, robust = 0, mycoption = 1, initializationList = list(), initializationList2 = list(), ell1 = 0.05, nboot = 10, nsamp = 1, doseg = FALSE, priorWeight = 0, verbose = FALSE, estimateSparseness = 0.2 )
| inmatrix | input as inmatrix=list(mat1,mat2). n by p input matrix and n by q input matrix , spatial variable lies along columns. |
|---|---|
| inmask | optional pair of antsImage masks |
| sparseness | a c(.,.) pair of values e.g c(0.01,0.1) enforces an unsigned 99 percent and 90 percent sparse solution for each respective view |
| nvecs | number of eigenvector pairs |
| its | number of iterations, 10 or 20 usually sufficient |
| cthresh | cluster threshold pair |
| statdir | temporary directory if you want to look at full output |
| perms | number of permutations |
| uselong | enforce solutions of both views to be the same - requires matrices to be the same size |
| z | subject space (low-dimensional space) sparseness value |
| smooth | smooth the data (only available when mask is used) |
| robust | rank transform input matrices |
| mycoption | enforce 1 - spatial orthogonality, 2 - low-dimensional orthogonality or 0 - both |
| initializationList | initialization for first view |
| initializationList2 | initialization for 2nd view |
| ell1 | gradient descent parameter, if negative then l0 otherwise use l1 |
| nboot | n bootstrap runs |
| nsamp | number of samples e.g. 0.9 indicates 90 percent of data |
| doseg | boolean to control matrix orthogonality during bootstrap |
| priorWeight | Scalar value weight on prior between 0 (prior is weak) and 1 (prior is strong). Only engaged if initialization is used |
| verbose | activates verbose output to screen |
| estimateSparseness | effect size to estimate sparseness per vector |
outputs a decomposition of a pair of matrices
Avants BB
if (FALSE) { mat<-replicate(100, rnorm(20)) mat2<-replicate(100, rnorm(20)) mydecom<-sparseDecom2boot( inmatrix=list(mat,mat2), sparseness=c(0.1,0.3) , nvecs=3, its=3, perms=0) wt<-0.666 mat3<-mat*wt+mat2*(1-wt) mydecom<-sparseDecom2boot( inmatrix=list(mat,mat3), sparseness=c(0.2,0.2), nvecs=5, its=10, perms=200 ) }