anovagamVoxel: 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 models after running a Generalized Additive Model (from mgcv::gam). The analysis will run in all voxels in the mask and will return the analysis of variance table for each voxel. Please check the mgcv::anova.gam documentation for further information about specific arguments used in anova.gam. Multi-model calls are disabled.

1
2
3

anovagamVoxel(image, mask, fourdOut = NULL, formula, 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 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 gam()
`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`	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 gam()

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

image <- oro.nifti::nifti(img = array(1:200, dim =c(2,2,2,25)))
mask <- oro.nifti::nifti(img = array(0:1, dim = c(2,2,2,1)))
set.seed(1)
covs <- data.frame(x = runif(25), y=runif(25))
fm1 <- "~ s(x) + y"
models <- anovagamVoxel(image=image, mask=mask,
              formula=fm1, subjData=covs, ncores = 1)