sparseRegression¶

purpose:

Sparse regression on input images.

description:

Compute a sparse, spatially coherent regression from a set of input images (with mask) to an outcome variable.

usage:

sparseRegression(inmatrix, demog, outcome, mask=NA, sparseness=0.05, nvecs=50,

its=5, cthresh=250, statdir=NA, z=0, smooth=0)

examples:

nsubj <- 1000

prop.train <- 1/2

subj.train <- sample(1:nsubj, prop.train*nsubj, replace=F)

input <- t(replicate(nsubj, rnorm(125)))

outcome <- seq(1, 5, length.out=nsubj)

demog <- data.frame(outcome=outcome)

input[, 40:60] <- 30 + outcome + rnorm(length(input[, 40:60]), sd=2)

input.train <- input[subj.train, ]

input.test <- input[-subj.train, ]

demog.train <- data.frame(outcome=demog[subj.train, ])

demog.test <- data.frame(outcome=demog[-subj.train, ])

mymask <- as.antsImage(array(rep(1, 125), dim=c(5,5,5)))

myregression <- sparseRegression(input.train, demog.train, "outcome", mymask,

    sparseness=0.05, nvecs=5, its=3, cthresh=250)

# visualization of results

sample <- rep(0, 125)

sample[40:60] <-1

signal.img <- as.antsImage(array(rep(0,125), dim=c(5, 5, 5)))

signal.img[signal.img >= 0 ] <- sample

plotANTsImage( signal.img, axis=2, slices="1x5x1") # actual source of signal

# compare against first learned regression vector

myimgs <- list()

for( i in 1:5){

  myarray <- as.array(myregression$eigenanatomyimages[[ i ]])

  myarray <- myarray / max(abs(myarray)) # normalize for visualization

  myimgs[[ i ]] <- antsImageClone(myregression$eigenanatomyimages[[ i ]])

  myimgs[[ i ]][mymask > 0] <- myarray

}

plotANTsImage(myimgs[[1]], axis=2, slices="1x5x1")

# use learned eigenvectors for prediction

result <- regressProjections(input.train, input.test, demog.train,

    demog.test, myregression$eigenanatomyimages, mymask, "outcome")

plot(result$outcome.comparison$real, result$outcome.comparison$predicted)

ANTsR 0.0.0 documentation

sparseRegression

sparseRegression¶