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

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.

anovagammVoxel(image, mask, fourdOut = NULL, formula, randomFormula, 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.
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()

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(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)
#> [1] "Created time series to matrix"
#> [1] "Created formula list"
#> [1] "Running test ANOVA"
#> [1] "Running parallel ANOVAs"
#> elapsed 
#>   0.571 
#> [1] "Parallel ANOVAs Ran"