Computes voxelwise analysis of variance (ANOVA) tables for a Generalized Additive Model.

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.

anovagamVoxel(image, mask, fourdOut = NULL, formula, subjData,
  dispersion = NULL, freq = FALSE, 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 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()

Value

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

Examples

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)
#> [1] "Created time series to matrix"
#> [1] "Created formula list"
#> [1] "Running test ANOVA"
#> [1] "Running parallel ANOVAs"
#> elapsed 
#>    0.07 
#> [1] "Parallel ANOVAs Ran"