sparseRegression: Sparse regression on input images.

In stnava/itkImageR: Examples using Rcpp to interface R and ITK

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)

Arguments

inmatrix	Input data matrix, with dimension number of subjects by number of voxels.
demog	Input demographics data frame. Contains outcome variable to regress against.
outcome	Name of column in demog to regress against.
mask	Mask for reconstructing inmatrix in physical space.
sparseness	Level of sparsity desired. 0.05, for example, makes 5% of the matrix be non-zero.
nvecs	Number of eigenvectors to return.
its	Number of cross-validation folds to run.
cthresh	Cluster threshold.
statdir	Where to put results. If not provided, a temp directory is created.
z	Row (subject-wise) sparseness.
smooth	Amount of smoothing.

Value

A list of values:

eigenanatomyimages	Coefficient vector images.
umatrix	Projections of input images on the sparse regression vectors. Can be used for, e.g., subsequent classification/predictions.
projections	Predicted values of outcome variable.

Author(s)

Kandel BM, Avants BB.

References

Kandel B.M., D. Wolk, J. Gee, and B. Avants. Predicting Cognitive Data from Medical Images Using Sparse Linear Regression. Information Processing in Medical Imaging, 2013.

Examples

require(ANTsR)
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)

stnava/itkImageR documentation built on May 30, 2019, 7:21 p.m.