View source: R/sparseDecom2boot.R
sparseDecom2boot | R Documentation |
Decomposes 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
## 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)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.