Description Usage Arguments Value Author(s) Examples
View source: R/combineNuisancePredictors.R
Combine and select nuisance predictors to maximize
correlation between inmat
and target
.
1 2 3 4 5 6 7 8 9 10 11 |
inmat |
Input predictor matrix. |
target |
Target outcome matrix. |
globalpredictors |
Global predictors of size |
maxpreds |
Maximum number of predictors to output. |
localpredictors |
Local predictor array of size |
method |
Method of selecting noisy voxels. One of 'svd' or 'cv'.
See |
k |
Number of cross-validation folds. |
covariates |
Covariates to be considered when assessing prediction
of |
ordered |
Can the predictors be assumed to be ordered from most important to least important, as in output from PCA? Computation is much faster if so. |
Array of size nrow(aslmat)
by npreds
,
containing a timeseries of all the nuisance predictors.
If localpredictors
is not NA, array is of size nrow(aslmat)
by ncol(aslmat)
by npreds
.
Benjamin M. Kandel, Brian B. Avants
1 2 3 4 5 6 7 8 9 | set.seed(120)
simimg <- makeImage( c(10,10,10,20) , rnorm( 10*10*10*20)+1 )
moco <- antsMotionCalculation( simimg , moreaccurate=0)
# for real data use below
# moco <- antsMotionCalculation(getANTsRData("pcasl"))
aslmat <- timeseries2matrix(moco$moco_img, moco$moco_mask)
tc <- rep(c(0.5, -0.5), length.out=nrow(aslmat))
noise <- getASLNoisePredictors(aslmat, tc, 0.5 )
noise.sub <- combineNuisancePredictors(aslmat, tc, noise, 2)
|
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