voxel (version 1.3.5)

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

Description

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.

Usage

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

# NOT RUN {

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)

# }