gamNIfTI: Wrapper to run a Generalized Additive model on a NIfTI image...

In angelgar/voxel: Mass-Univariate Voxelwise Analysis of Medical Imaging Data

Description

This function is able to run a Generalized Additive Model (GAM) using the mgcv package. The analysis will run in all voxels in in the mask and will return parametric and smooth coefficients. The function will create parametric maps according to the model selected. The function will return a p-map, t-map, z-map, p-adjusted-map for parametric terms and p-map, z-map, p-adjusted-map for smooth terms. You can select which type of p-value correction you want done on the map.

Usage

gamNIfTI(image, mask, fourdOut = NULL, formula, subjData,
  mc.preschedule = TRUE, ncores = 1, method = "none", residual = FALSE,
  outDir = NULL, ...)

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. 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 gam()
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
method	which method of correction for multiple comparisons (default is none)
residual	If set to TRUE then residuals maps will be returned along parametric maps
outDir	Path to the folder where to output parametric maps (Default is Null, only change if you want to write maps out)
...	Additional arguments passed to gam()

Value

Parametric maps of the fitted models

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 = rnorm(25))
fm1 <- "~ x + s(y)"
Maps <- gamNIfTI(image=image, mask=mask, 
              formula=fm1, subjData=covs, ncores = 1, method="fdr")

angelgar/voxel documentation built on Dec. 31, 2019, 3:09 p.m.