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