Description Usage Arguments Value Author(s) Examples
Perform cross-validation on an image set using eigencomponents to predict an outcome variable.
1 2 | cvEigenanatomy(demog, images, outcome, ratio=10, mask=NA,
sparseness=0.01, nvecs=50, its=5, cthresh=250, ...)
|
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 |
ratio |
If greater than 1, number of folds for cross-validation. If less than 1, one testing-training step will be performed, using |
mask |
Mask image of type |
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 |
A result, or (if ratio > 1) list of results, from regressProjection
.
Kandel BM and Avants B
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | ## Not run:
# 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)
## End(Not run)
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