sparseDecom2.RdDecomposes two matrices into paired sparse eigenevectors to maximize canonical correlation. Note: we do not scale the matrices internally. We leave scaling choices to the user.
sparseDecom2(
inmatrix,
inmask = list(NULL, NULL),
sparseness = c(0.01, 0.01),
nvecs = 3,
its = 20,
cthresh = c(0, 0),
statdir = NA,
perms = 0,
uselong = 0,
z = 0,
smooth = 0,
robust = 0,
mycoption = 0,
initializationList = list(),
initializationList2 = list(),
ell1 = 10,
priorWeight = 0,
verbose = FALSE,
rejector = 0,
maxBased = FALSE
)input as inmatrix=list(mat1,mat2). n by p input matrix and n by q input matrix , spatial variable lies along columns.
optional pair of antsImage masks
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
number of eigenvector pairs
number of iterations, 10 or 20 usually sufficient
cluster threshold pair
temporary directory if you want to look at full output
number of permutations. settings permutations greater than 0 will estimate significance per vector empirically. For small datasets, these may be conservative. p-values depend on how one scales the input matrices.
enforce solutions of both views to be the same - requires matrices to be the same size
subject space (low-dimensional space) sparseness value
smooth the data (only available when mask is used)
rank transform input matrices
enforce 1 - spatial orthogonality, 2 - low-dimensional orthogonality or 0 - both
initialization for first view
initialization for 2nd view
gradient descent parameter, if negative then l0 otherwise use l1
Scalar value weight on prior between 0 (prior is weak) and 1 (prior is strong). Only engaged if initialization is used
activates verbose output to screen
rejects small correlation solutions
boolean that chooses max-based thresholding
outputs a decomposition of a pair of matrices
mat <- replicate(100, rnorm(20))
mat2 <- replicate(100, rnorm(20))
mat <- scale(mat)
mat2 <- scale(mat2)
mydecom <- sparseDecom2(
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 <- sparseDecom2(
inmatrix = list(mat, mat3),
sparseness = c(0.2, 0.2), nvecs = 5, its = 10, perms = 5
)
if (FALSE) { # \dontrun{
# a masked example
im <- antsImageRead(getANTsRData("r64"))
mask <- thresholdImage(im, 250, Inf)
dd <- sum(mask == 1)
mat1 <- matrix(rnorm(dd * 10), nrow = 10)
mat2 <- matrix(rnorm(dd * 10), nrow = 10)
initlist <- list()
for (nvecs in 1:2) {
init1 <- antsImageClone(mask)
init1[mask == 1] <- rnorm(dd)
initlist <- lappend(initlist, init1)
}
ff <- sparseDecom2(
inmatrix = list(mat1, mat2), inmask = list(mask, mask),
sparseness = c(0.1, 0.1), nvecs = length(initlist), smooth = 1,
cthresh = c(0, 0), initializationList = initlist, ell1 = 11
)
### now SNPs ###
rf <- usePkg("randomForest")
bg <- usePkg("BGLR")
if (bg & rf) {
data(mice)
snps <- mice.X
numericalpheno <- as.matrix(mice.pheno[, c(4, 5, 13, 15)])
numericalpheno <- residuals(lm(numericalpheno ~
as.factor(mice.pheno$Litter)))
nfolds <- 6
train <- sample(rep(c(1:nfolds), 1800 / nfolds))
train <- (train < 4)
snpd <- sparseDecom2(
inmatrix = list(
(as.matrix(snps[train, ])),
numericalpheno[train, ]
), nvecs = 20, sparseness = c(0.001, -0.5),
its = 3, ell1 = 0.1, z = -1
)
for (j in 3:3) {
traindf <- data.frame(
bmi = numericalpheno[train, j],
snpse = as.matrix(snps[train, ]) %*% as.matrix(snpd$eig1)
)
testdf <- data.frame(
bmi = numericalpheno[!train, j],
snpse = as.matrix(snps[!train, ]) %*% as.matrix(snpd$eig1)
)
myrf <- randomForest(bmi ~ ., data = traindf)
preddf <- predict(myrf, newdata = testdf)
print(cor.test(preddf, testdf$bmi))
plot(preddf, testdf$bmi)
}
} # check bg and rf
} # }