Cross-validation method for eigenanatomy decompositions.

Perform cross-validation on an image set using eigencomponents to predict an outcome variable.

cvEigenanatomy(
  demog,
  images,
  outcome,
  ratio = 10,
  mask = NULL,
  sparseness = 0.01,
  nvecs = 50,
  its = 5,
  cthresh = 250,
  ...
)

Arguments

demog	Demographics information that includes outcome and (optional) covariates.
images	n by p input image matrix, where n is the number of subjects and p is the number of voxels.
outcome	Name of outcome variable. Must be present in demog.
ratio	If greater than 1, number of folds for cross-validation. If less than 1, one testing-training step will be performed, using ratio of the data for training and the rest for testing.
mask	Mask image of type antsImage.
sparseness	Desired level of sparsity in decomposition.
nvecs	Number of eigenvectors to use in decomposition.
its	Number of iterations for decomposition.
cthresh	Cluster threshold for decomposition.
...	Additional options passed to regressProjections.

Value

A result, or (if ratio > 1) list of results, from regressProjection.

Author

Kandel BM and Avants B

Examples

if (FALSE) {
# generate simulated outcome
nsubjects <- 100
x1 <- seq(1, 10, length.out=nsubjects) + rnorm(nsubjects, sd=2)
x2 <- seq(25, 15, length.out=nsubjects) + rnorm(nsubjects, sd=2)
outcome <- 3 * x1 + 4 * x2 + rnorm(nsubjects, sd=1)
# generate simulated images with outcome predicted 
# by sparse subset of voxels
voxel.1 <- 3 * x1 + rnorm(nsubjects, sd=2)
voxel.2 <- rnorm(nsubjects, sd=2)
voxel.3 <- 2 * x2 + rnorm(nsubjects, sd=2)
voxel.4 <- rnorm(nsubjects, sd=3)
input   <- cbind(voxel.1, voxel.2, voxel.3, voxel.4)
mask    <- as.antsImage(matrix(c(1,1,1,1), nrow=2))
# generate sample demographics that do not explain outcome
age <- runif(nsubjects, 50, 75)
demog <- data.frame(outcome=outcome, age=age)
result <- cvEigenanatomy(demog, input, 'outcome', ratio=5, mask, 
            sparseness=0.25, nvecs=4) 
}