Description Usage Arguments Value Author(s)
View source: R/optimize_SCCANsparseness.R
Function used to optimize SCCAN sparseness for lesion to symptom mapping.
1 2 3 4 5 6 7 8 9 | optimize_SCCANsparseness(lesmat, behavior, mask, nFolds = 4,
sparsenessPenalty = 0.03, lowerSparseness = -0.9,
upperSparseness = 0.9, tol = 0.03, justValidate = FALSE,
cvRepetitions = ifelse(length(behavior) <= 30, 6,
ifelse(length(behavior) <= 40, 5, ifelse(length(behavior) <= 50, 4, 3))),
showInfo = TRUE, directionalSCCAN = TRUE, mycoption = 1,
robust = 1, sparseness = NA, nvecs = 1, cthresh = 150,
its = 30, npermsSCCAN = 0, smooth = 0.4,
sparseness.behav = -0.99, maxBased = FALSE, ...)
|
lesmat |
lesion matrix |
behavior |
behavior vector |
mask |
antsImage mask |
nFolds |
how many folds to use |
sparsenessPenalty |
penalty term |
lowerSparseness |
minimum searched sparseness |
upperSparseness |
maximum searched sparseness |
tol |
tolerance value, see |
justValidate |
just check the CV of provided sparseness |
cvRepetitions |
number of cross-validations at each sparseness value. Dynamically set depending on sample size: <=30 to 6 reps, <=40 to 5 reps, <=50 to 4 reps, > 50 to 3 reps. |
showInfo |
logical (default=TRUE) display messages |
directionalSCCAN |
(default=TRUE) switching to FALSE will switch sparseness range in the positive side, 0.005 to 0.9 |
mycoption |
standard SCCAN parameter |
robust |
standard SCCAN parameter |
sparseness |
standard SCCAN parameter |
nvecs |
standard SCCAN parameter |
cthresh |
standard SCCAN parameter |
its |
standard SCCAN parameter |
npermsSCCAN |
SCCAN permutations |
smooth |
standard SCCAN parameter |
sparseness.behav |
what sparsness to use for behavior |
maxBased |
standard SCCAN parameter |
... |
other arguments received from |
List with:
minimum
- best sparseness value
objective
- minimum value of objective function
CVcorrelation
- cross-validated correlation of optimal sparness
Dorian Pustina
the optimization function Will run SCCAN on each training fold, compute behavior prediction on the test fold, and finally return a cross validated correlation from entire sample
end of optimfun
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