gammVoxel: Run a Generalized Additive Mixed Effects Model on all voxels...

In neuroconductor/voxel: Mass-Univariate Voxelwise Analysis of Medical Imaging Data

Description

This function is able to run a Generalized Mixed Effects Model (GAMM) using the gamm4() function. The analysis will run in all voxels within the mask and will return the model fit for each voxel.

Usage

gammVoxel(image, mask, fourdOut = NULL, formula, randomFormula, subjData,
  mc.preschedule = TRUE, ncores = 1, ...)

Arguments

image	Input image of type 'nifti' or vector of path(s) to images. If multiple paths, the script will call mergeNifti() and merge across time.
mask	Input mask of type 'nifti' or path to mask. Must be a binary mask
fourdOut	To be passed to mergeNifti, This is the path and file name without the suffix to save the fourd file. Default (NULL) means script won't write out 4D image.
formula	Must be a formula passed to gamm4()
randomFormula	Random effects formula passed to gamm4()
subjData	Dataframe containing all the covariates used for the analysis
mc.preschedule	Argument to be passed to mclapply, whether or not to preschedule the jobs. More info in parallel::mclapply
ncores	Number of cores to use
...	Additional arguments passed to gamm4()

Value

Returns list of models fitted to each voxel over the masked images passed to function.

Examples

image <- oro.nifti::nifti(img = array(1:1600, dim =c(4,4,4,25)))
mask <- oro.nifti::nifti(img = array(c(rep(0,14),1), dim = c(4,4,4,1)))
set.seed(1)
covs <- data.frame(x = runif(25), id = rep(1:5,5))
fm1 <- "~ s(x)"
randomFormula <- "~(1|id)"
models <- gammVoxel(image = image , mask = mask, formula = fm1, randomFormula = randomFormula, 
                                                       subjData = covs, ncores = 1, REML=TRUE)

neuroconductor/voxel documentation built on May 20, 2021, 9:39 p.m.