lmCluster {voxel} | R Documentation |
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)