rparMap: Create parametric maps and residuals
In neuroconductor/voxel: Mass-Univariate Voxelwise Analysis of Medical Imaging Data

Description Usage Arguments Value Examples

This function create parametric maps according from model parametric tables or analysis of variance tables. The function will return a p-map, t-map, signed z-map, p-adjusted-map for parametric terms and p-map, z-map, p-adjusted-map for smooth terms. Additionally the function will return a p-map, F-map, p-to-z-map, and p-adjusted-map if the input is ANOVA. This function will return a residual map that can be used for cluster correction You can select which type of p-value correction you want done on the map. The z-maps are signed just like FSL.

1 2	rparMap(parameters, image, mask, method, ncores, mc.preschedule, outDir = NULL)

`parameters`	list of parametric and smooth table coefficients or ANOVA (like the output from vlmParam, vgamParam, anovalmVoxel)
`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 one. Must be a binary mask or a character. Must match the mask passed to one of vlmParam, vgamParam, vgamm4Param, vlmerParam
`method`	which method of correction for multiple comparisons (default is none)
`ncores`	Number of cores to use
`mc.preschedule`	Argument to be passed to mclapply, whether or not to preschedule the jobs. More info in parallel::mclapply
`outDir`	Path to the folder where to output parametric maps (Default is Null, only change if you want to write maps out)

Return parametric maps of the fitted models

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)
Maps <- rparMap(models, image, mask, method="fdr", ncores = 1, mc.preschedule=TRUE)