Description Usage Arguments Value Author(s) Examples
View source: R/sparseDecom2boot.R
Decomposes two matrices into paired sparse eigenevectors to maximize canonical correlation.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | 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
1 2 3 4 5 6 7 8 9 10 11 12 | ## Not run:
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 )
## End(Not run)
|
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