Combine and reduce dimensionality of nuisance predictors.

Combine and select nuisance predictors to maximize correlation between inmat and target.

combineNuisancePredictors(
  inmat,
  target,
  globalpredictors = NA,
  maxpreds = 4,
  localpredictors = NA,
  method = "cv",
  k = 5,
  covariates = NA,
  ordered = FALSE
)

Arguments

inmat	Input predictor matrix.
target	Target outcome matrix.
globalpredictors	Global predictors of size nrow(inmat) by n, where n is the number of global predictors.
maxpreds	Maximum number of predictors to output.
localpredictors	Local predictor array of size nrow(inmat) by ncol(inmat) by m, where m is the number of local predictors.
method	Method of selecting noisy voxels. One of 'svd' or 'cv'. See Details.
k	Number of cross-validation folds.
covariates	Covariates to be considered when assessing prediction of target.
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.

Value

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.

Author

Benjamin M. Kandel, Brian B. Avants

Examples

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)