lmCluster function

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

This function is able to run a Linear Model using the stats package. All clusters must be labeled with integers in the mask passed as an argument.

lmCluster(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 lm()
• 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 lm()

Returns

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(1:4, dim = c(4,4,4,1)))
set.seed(1)
covs <- data.frame(x = runif(25))
fm1 <- "~ x"
models <- lmCluster(image=image, mask=mask, 
              formula=fm1, subjData=covs, ncores = 1)