voxel (version 1.3.5)

gammNIfTI: Wrapper to run a Generalized Additive Mixed Effects model on an Nifti and output a parametric map

Description

This function is able to run a Generalized Additive Model (GAMM) using the gamm4() function. The analysis will run in all voxels within 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

gammNIfTI(image, mask, fourdOut = NULL, formula, randomFormula, 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 gamm4()

randomFormula

Random effects formula passed to gamm4()

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 gamm4()

Value

Returns Parametric maps of the fitted models over the NIfTI image

Examples

# NOT RUN {
image <- oro.nifti::nifti(img = array(rnorm(1600, sd=10), dim =c(4,4,4,25)))
mask <- oro.nifti::nifti(img = array(c(rep(0,14), rep(1,2)), dim = c(4,4,4,1)))
set.seed(1)
covs <- data.frame(x = runif(25), y = runif(25), id = rep(1:5,5))
fm1 <- "~ s(x) + s(y)"
randomFormula <- "~(1|id)"
Maps <- gammNIfTI(image, mask, formula = fm1, 
                 randomFormula = randomFormula, subjData = covs, ncores = 1,
                 method="fdr", REML=TRUE)


# }