combineNuisancePredictors: Combine and reduce dimensionality of nuisance predictors.
In neuroconductor/ANTsR: ANTs in R: Quantification Tools for Biomedical Images

Description Usage Arguments Value Author(s) Examples

View source: R/combineNuisancePredictors.R

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
)

`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.

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

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)