rlmParam {voxel} | R Documentation |
Run a Linear Model on all voxels of a NIfTI and return parametric coefficients and residuals
Description
This function is able to run a Linear Model using the stats package. The analysis will run in all voxels in in the mask and will and will return parametric coefficients at each voxel.
Usage
rlmParam(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 all mergeNifti() and merge across time. |
mask |
Input mask of type 'nifti' or path to mask. Must be a binary mask |
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
Return list of parametric and spline coefficients (include standard errors and p-values) 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(0:1, dim = c(4,4,4,1)))
set.seed(1)
covs <- data.frame(x = runif(25), y = runif(25))
fm1 <- "~ x + y"
models <- rlmParam(image=image, mask=mask, formula=fm1, subjData=covs, ncores = 1)