Get nuisance predictors from ASL images

Get nuisance predictors from ASL images

getASLNoisePredictors(
  aslmat,
  tc,
  noisefrac = 0.1,
  polydegree = "loess",
  k = 5,
  npreds = 12,
  method = "noisepool",
  covariates = NA,
  noisepoolfun = max
)

Arguments

aslmat	ASL input matrix.
tc	Tag-control sawtooth pattern vector.
noisefrac	Fraction of data to include in noise pool.
polydegree	Degree of polynomial for detrending, with a value of 0 indicating no detrending, or 'loess' for LOESS-based estimation of global time-series trends.
k	Number of cross-validation folds.
npreds	Number of predictors to output.
method	Method of selecting noisy voxels. One of 'compcor' or 'noisepool'. See Details.
covariates	Covariates to be considered when assessing prediction of tc pattern.
noisepoolfun	Function used for aggregating R^2 values.

Value

Matrix of size nrow(aslmat) by npreds, containing a timeseries of all the nuisance predictors.

Author

Brian B. Avants, Benjamin M. Kandel

Examples

# for real data do img<-antsImageRead(getANTsRData("pcasl"),4)
set.seed(120)
img<-makeImage( c(10,10,10,20), rnorm(1000*20)+1 )
mask = getMask( getAverageOfTimeSeries( img ) )
aslmat <- timeseries2matrix( img, mask )
tc <- rep(c(0.5, -0.5), length.out=nrow(aslmat))
noise <- getASLNoisePredictors(aslmat, tc, k=2, npreds=2, noisefrac=0.5 )
cm = colMeans(noise)
rounding_type = RNGkind()[3]
if (getRversion() < "3.6.0" || rounding_type == "Rounding") {
    testthat::expect_equal(cm, c(-0.223292128499263, 0.00434481670243642), tolerance = .01 )
} else {
    testthat::expect_equal(cm, c(-0.223377249912075, 0.0012754214030999), tolerance = .01)
}