View source: R/cvEigenanatomy.R
cvEigenanatomy | R Documentation |
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,
...
)
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
## 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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