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) { # \dontrun{
# 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
)
} # }