Run a Linear Mixed Effects Model on the mean intensity over a region of interest

This function is able to run a LME using the lmer() function. All clusters or region of interest must be labeled with integers in the mask passed as an argument. The function relies on lmerTest to create p-values using the Satterthwaite Approximation.

lmerCluster(image, mask, fourdOut = NULL, formula, 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. All clusters must be labeled with integers in the mask passed as an argument
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 lmer()
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 lmer()

Value

Returns list of models fitted to the mean voxel intensity a region or interest.

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),1,2), dim = c(4,4,4,1)))
set.seed(1)
covs <- data.frame(x = runif(25), id = rep(1:5,5))
fm1 <- "~ x + (1|id)"
models <- lmerCluster(image, mask, formula = fm1, subjData = covs, ncores = 1, REML=TRUE)
#> [1] "Created meanCluster Matrix"
#> [1] "Created formula list"
#> [1] "Running test model"
#> boundary (singular) fit: see ?isSingular
#> [1] "Running parallel models"
#> boundary (singular) fit: see ?isSingular
#> boundary (singular) fit: see ?isSingular
#> elapsed 
#>    0.09 
#> [1] "Parallel Models Ran"