anovagammVoxel: Computes voxelwise analysis of variance (ANOVA) tables for a...
In voxel: Mass-Univariate Voxelwise Analysis of Medical Imaging Data

Description Usage Arguments Value Examples

This function computes analysis of variance tables for the fitted Generalized Additive Mixed Effects (from gamm4::gamm4) models. The analysis will run in all voxels in the specified mask and will return a list with the ANOVA table at each voxel. Please check the mgcv::anova.gam documentation for further information about specific arguments used in anova.gam. Multi-model calls are disabled.

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anovagammVoxel(image, mask, fourdOut = NULL, formula, randomFormula, subjData,
  dispersion = NULL, freq = FALSE, mc.preschedule = TRUE, ncores = 1,
  ...)

`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.
`fourdOut`	To be passed to mergeNifti, This is the output path to write out the fourd file. Do not include a suffix (i.e. .nii.gz). 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
`dispersion`	To be passed to mgcv::anova.gam, Defaults to NULL. Dispersion Parameter, not normally used.
`freq`	To be passed to mgcv::anova.gam, Defaults to FALSE. Frequentist or Bayesian approximations for p-values
`mc.preschedule`	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()

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

image <- oro.nifti::nifti(img = array(1:1600, dim =c(4,4,4,25)))
mask <- oro.nifti::nifti(img = array(data = c(rep(0,15), rep(1,1)),
                                     dim = c(4,4,4,1)))
set.seed(1)
covs <- data.frame(x = runif(25), y=runif(25), id = rep(1:5,5))
f1 <- "~ s(x) + y"
randomFormula <- "~(1|id)"
models <- anovagammVoxel(image, mask, formula = f1,
                              randomFormula = randomFormula,
                              subjData = covs, ncores = 1, REML=TRUE)