ANTsR (version 0.3.3)

sparseRegression: 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 = 10, 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: A list of values:

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. literature/web site here ~

Examples

## Not run: 
# 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
# plot( 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
# }
# plot(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)
# ## End(Not run)