View source: R/networkEiganat.R
networkEiganat | R Documentation |
Decomposes a matrix into sparse eigenevectors to maximize explained variance.
networkEiganat(
Xin,
sparseness = c(0.1, 0.1),
nvecs = 5,
its = 5,
gradparam = 1,
mask = NA,
v,
prior,
pgradparam = 0.1,
clustval = 0,
downsample = 0,
doscale = T,
domin = T,
verbose = F,
dowhite = 0,
timeme = T,
addb = T,
useregression = T
)
Xin |
n by p input images , subjects or time points by row , spatial variable lies along columns |
sparseness |
sparseness pair c( 0.1 , 0.1 ) |
nvecs |
number of vectors |
its |
number of iterations |
gradparam |
gradient descent parameter for data |
mask |
optional antsImage mask |
v |
the spatial solultion |
prior |
the prior |
pgradparam |
gradient descent parameter for prior term |
clustval |
integer greater than or equal to zero |
downsample |
bool |
doscale |
bool |
domin |
bool |
verbose |
bool |
dowhite |
bool |
timeme |
bool |
addb |
bool |
useregression |
bool |
outputs a decomposition of a population or time series matrix
Avants BB
## Not run:
mat <- replicate(100, rnorm(20))
mydecom <- networkEiganat(mat, nvecs = 5)
ch1 <- usePkg("randomForest")
ch2 <- usePkg("BGLR")
if (ch1 & ch2) {
data(mice)
snps <- quantifySNPs(mice.X)
numericalpheno <- as.matrix(mice.pheno[, c(4, 5, 13, 15)])
numericalpheno <- residuals(lm(numericalpheno ~
as.factor(mice.pheno$Litter)))
phind <- 3
nfolds <- 6
train <- sample(rep(c(1:nfolds), 1800 / nfolds))
train <- (train < 4)
lowr <- lowrankRowMatrix(as.matrix(snps[train, ]), 900)
snpdS <- sparseDecom(lowr, nvecs = 2, sparseness = (-0.001), its = 3)
snpdF <- sparseDecom(lowrankRowMatrix(as.matrix(snps[train, ]), 100),
nvecs = 2, sparseness = (-0.001), its = 3
)
projmat <- as.matrix(snpdS$eig)
projmat <- as.matrix(snpdF$eig)
snpdFast <- networkEiganat(as.matrix(snps[train, ]),
nvecs = 2,
sparseness = c(1, -0.001), downsample = 45, verbose = T, its = 3,
gradparam = 10
)
snpdSlow <- networkEiganat(as.matrix(snps[train, ]),
nvecs = 2,
sparseness = c(1, -0.001), downsample = 0, verbose = T,
its = 3, gradparam = 10
)
snpd <- snpdSlow
snpd <- snpdFast
projmat <- as.matrix(snpd$v)
snpdF <- sparseDecom(lowrankRowMatrix(as.matrix(snps[train, ]), 10),
nvecs = 2, sparseness = (-0.001), its = 3
)
projmat <- as.matrix(snpdS$eig)
snpse <- as.matrix(snps[train, ]) %*% projmat
traindf <- data.frame(bmi = numericalpheno[train, phind], snpse = snpse)
snpse <- as.matrix(snps[!train, ]) %*% projmat
testdf <- data.frame(bmi = numericalpheno[!train, phind], snpse = snpse)
myrf <- glm(bmi ~ ., data = traindf)
preddf <- predict(myrf, newdata = testdf)
cor.test(preddf, testdf$bmi)
} # ch1 and ch2
###########
## End(Not run)
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