lmCluster {voxel}

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

Description

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.

Usage

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()

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(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)