Computes voxelwise analysis of variance (ANOVA) tables for a Generalized Additive Model.

Description

This function computes analysis of variance tables for the fitted models after running a Generalized Additive Model (from mgcv::gam). The analysis will run in all voxels in the mask and will return the analysis of variance table for each voxel. Please check the mgcv::anova.gam documentation for further information about specific arguments used in anova.gam. Multi-model calls are disabled.

Usage

anovagamVoxel(image, mask, fourdOut = NULL, formula, subjData,
  dispersion = NULL, freq = FALSE, mc.preschedule = TRUE, ncores = 1,
  ...)

Arguments

Value

Returns list of models fitted to each voxel over the masked images passed to function.

Examples

image <- oro.nifti::nifti(img = array(1:200, dim =c(2,2,2,25)))
mask <- oro.nifti::nifti(img = array(0:1, dim = c(2,2,2,1)))
set.seed(1)
covs <- data.frame(x = runif(25), y=runif(25))
fm1 <- "~ s(x) + y"
models <- anovagamVoxel(image=image, mask=mask,
              formula=fm1, subjData=covs, ncores = 1)

Computes voxelwise analysis of variance (ANOVA) tables for a Generalized Additive Mixed Effects Model.

Description

This function computes analysis of variance tables for the fitted Generalized Additive Mixed Effects (from gamm4::gamm4) models. The analysis will run in all voxels in the specified mask and will return a list with the ANOVA table at each voxel. Please check the mgcv::anova.gam documentation for further information about specific arguments used in anova.gam. Multi-model calls are disabled.

Usage

anovagammVoxel(image, mask, fourdOut = NULL, formula, randomFormula, subjData,
  dispersion = NULL, freq = FALSE, mc.preschedule = TRUE, ncores = 1,
  ...)

Arguments

Value

Returns list of models 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(data = c(rep(0,15), rep(1,1)),
                                     dim = c(4,4,4,1)))
set.seed(1)
covs <- data.frame(x = runif(25), y=runif(25), id = rep(1:5,5))
f1 <- "~ s(x) + y"
randomFormula <- "~(1|id)"
models <- anovagammVoxel(image, mask, formula = f1,
                              randomFormula = randomFormula,
                              subjData = covs, ncores = 1, REML=TRUE)

Computes voxelwise analysis of variance (ANOVA) tables for a Linear Model.

Description

This function computes analysis of variance tables for the fitted models after running a Linear Model using the stats::lm() function. The analysis will run in all voxels in the mask and will return the analysis of variance table for each voxel. Please check the stats documentation for further information about specific arguments used in stats::anova.lm(). Multi-model calls are disabled.

Usage

anovalmVoxel(image, mask, fourdOut = NULL, formula, subjData,
  mc.preschedule = TRUE, ncores = 1, ...)

Arguments

Value

Returns list of models 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 <- anovalmVoxel(image=image, mask=mask,
              formula=fm1, subjData=covs, ncores = 1)

Computes voxelwise analysis of variance (ANOVA) tables for a Linear Mixed Effects Model.

Description

This function computes analysis of variance tables for the fitted models after running a Linear Mixed Effect Model using the lmerTest() function and the anova function in that package. The analysis will run in all voxels in the mask and will return the analysis of variance table for each voxel. Please check the lmerTest documentation for further information about specific arguments used in anova.lmerModLmerTest. Multi-model calls are disabled.

Usage

anovalmerVoxel(image, mask, fourdOut = NULL, formula, subjData,
  ddf = "Satterthwaite", type = 3, mc.preschedule = TRUE, ncores = 1,
  ...)

Arguments

Value

Returns list of models 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(c(rep(0,15), rep(1,1)), dim = c(4,4,4,1)))
set.seed(1)
covs <- data.frame(x = runif(25), y = runif(25), id = rep(1:5,5))
fm1 <- "~ x + y + (1|id)"
models <- anovalmerVoxel(image, mask, formula = fm1, subjData = covs, ncores = 1, REML=TRUE)

Run a Generalized Additive Model on the mean intensity over a region of interest

Description

This function is able to run a Generalized Additive Model (GAM) using the mgcv package. All clusters must be labeled with integers in the mask passed as an argument.

Usage

gamCluster(image, mask, fourdOut = NULL, formula, subjData,
  mc.preschedule = TRUE, ncores = 1, ...)

Arguments

Value

Returns list of models fitted to the mean voxel intensity over region of interest.

Examples

image <- oro.nifti::nifti(img = array(1:1600, dim =c(4,4,4,25)))
mask <- oro.nifti::nifti(img = array(1:4, dim = c(4,4,4,1)))
set.seed(1)
covs <- data.frame(x = runif(25))
fm1 <- "~ s(x)"
models <- gamCluster(image=image, mask=mask, 
              formula=fm1, subjData=covs, ncores = 1, method="REML")

Wrapper to run a Generalized Additive model on a NIfTI image and output parametric maps

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

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")

Generate FSL Randomise call for a GAM Model

Description

This function is able to generate all the necessary files to run randomise with a GAM Model This script will write out all design and contrast files This function will run a f-test to compare a full and reduced model (a model with and without spline)

Usage

gamRandomise(image, maskPath = NULL, formulaFull, formulaRed, subjData,
  outDir, nsim = 500, thresh = 0.01, run = FALSE)

Arguments

Value

Return randomise command

Examples

## Not run: 

subjData = mgcv::gamSim(1,n=400,dist="normal",scale=2)
OutDirRoot="Output Directory"
maskName="Path to mask"
imagePath="Path to output"
covsFormula="~s(age,k=5)+sex+mprage_antsCT_vol_TBV"
redFormula="~sex+mprage_antsCT_vol_TBV"

gamRandomise(image = imagePath, maskPath = maskName, formulaFull = covsFormula, 
           formulaRed = redFormula, subjData = subjData, outDir = OutDirRoot)


## End(Not run)

Run a Generalized Additive Model on all voxels of a NIfTI image within a mask.

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 the model fit for each voxel.

Usage

gamVoxel(image, mask, fourdOut = NULL, formula, subjData,
  mc.preschedule = TRUE, ncores = 1, ...)

Arguments

Value

List of models 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))
fm1 <- "~ s(x)"
models <- gamVoxel(image=image, mask=mask, 
              formula=fm1, subjData=covs, ncores = 1)

Run a Generalized Additive Mixed Effects Model on the mean intensity over a region of interest

Description

This function is able to run a Generalized Additive Mixed Effects Model (GAMM) using the gamm4() function. All clusters or Regions of Interest must be labeled with integers in the mask passed as an argument.

Usage

gammCluster(image, mask, fourdOut = NULL, formula, randomFormula, subjData,
  mc.preschedule = TRUE, ncores = 1, ...)

Arguments

Value

Returns list of models fitted to the mean voxel intensity a region or interest.

Examples

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 <- "~ s(x)"
randomFormula <- "~(1|id)"
models <- gammCluster(image, mask, formula = fm1, 
                              randomFormula = randomFormula, subjData = covs, ncores = 1, REML=TRUE)
                              

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

Value

Returns Parametric maps of the fitted models over the NIfTI image

Examples

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)

Run a Generalized Additive Mixed Effects Model on all voxels of a NIfTI image within a mask.

Description

This function is able to run a Generalized Mixed Effects Model (GAMM) using the gamm4() function. The analysis will run in all voxels within the mask and will return the model fit for each voxel.

Usage

gammVoxel(image, mask, fourdOut = NULL, formula, randomFormula, subjData,
  mc.preschedule = TRUE, ncores = 1, ...)

Arguments

Value

Returns list of models 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(c(rep(0,14),1), dim = c(4,4,4,1)))
set.seed(1)
covs <- data.frame(x = runif(25), id = rep(1:5,5))
fm1 <- "~ s(x)"
randomFormula <- "~(1|id)"
models <- gammVoxel(image = image , mask = mask, formula = fm1, randomFormula = randomFormula, 
                                                       subjData = covs, ncores = 1, REML=TRUE)

Create list of Formulas for each voxel

Description

This function is internal. This function creates list of formulas that will be passed for analysis.

Usage

listFormula(x, formula)

Arguments

Examples

x <- 1
fm1 <- "~ x1"
formula <- listFormula(x, formula = fm1)

Run a Linear Model on the mean intensity over a region of interest

Description

This function is able to run a Linear Model using the stats package. All clusters must be labeled with integers in the mask passed as an argument.

Usage

lmCluster(image, mask, fourdOut = NULL, formula, subjData,
  mc.preschedule = TRUE, ncores = 1, ...)

Arguments

Value

Returns list of models fitted to the mean voxel intensity a region or interest.

Examples

image <- oro.nifti::nifti(img = array(1:1600, dim =c(4,4,4,25)))
mask <- oro.nifti::nifti(img = array(1:4, dim = c(4,4,4,1)))
set.seed(1)
covs <- data.frame(x = runif(25))
fm1 <- "~ x"
models <- lmCluster(image=image, mask=mask, 
              formula=fm1, subjData=covs, ncores = 1)

Wrapper to run a model on a NIfTI image and output parametric maps

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 return parametric 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

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

Arguments

Value

Return 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 = runif(25))
fm1 <- "~ x + y"
Maps <- lmNIfTI(image=image, mask=mask, 
              formula=fm1, subjData=covs, ncores = 1, method="fdr")

Run a Linear Model on all voxels of a NIfTI image within a mask.

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 return the model fit for each voxel.

Usage

lmVoxel(image, mask, fourdOut = NULL, formula, subjData,
  mc.preschedule = TRUE, ncores = 1, ...)

Arguments

Value

Returns list of models 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))
fm1 <- "~ x"
models <- lmVoxel(image=image, mask=mask, 
              formula=fm1, subjData=covs, ncores = 1)

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

Value

Returns list of models fitted to the mean voxel intensity a region or interest.

Examples

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)

Run a Linear Mixed Effects Model on a NIfTI image and output a parametric maps

Description

This function is able to run a Linear Mixed Effect Model using the lmer() function. The function relies on lmerTest to create p-values using the Satterthwaite Approximation. The analysis will run in all voxels in in the mask and will return parametric 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

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

Arguments

Value

Returns 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(c(rep(0,14),1,1), dim = c(4,4,4,1)))
set.seed(1)
covs <- data.frame(x = runif(25), id = rep(1:5,5))
fm1 <- "~ x + (1|id)"
Maps <- lmerNIfTI(image, mask, formula = fm1, subjData = covs, method="fdr", ncores = 1)

Run a Linear Mixed Effects Model on all voxels of a NIfTI image within a mask.

Description

This function is able to run a Linear Mixed Effect Model using the lmer() function. The analysis will run in all voxels in in the mask and will return parametric coefficients at each voxel The function relies on lmerTest to create p-values using the Satterthwaite Approximation.

Usage

lmerVoxel(image, mask, fourdOut = NULL, formula = NULL, subjData,
  mc.preschedule = TRUE, ncores = 1, ...)

Arguments

Value

returns list of models fitted to each voxel over the masked images passed to function.

Examples

## Not run: 

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), id = rep(1:5,5))
fm1 <- "~ x + (1|id)"
models <- lmerVoxel(image, mask, formula = fm1, subjData = covs, ncores = 1, REML=T)

## End(Not run)

Merge NIfTI Images across specified direction

Description

This function merges nifti images together in a specified direction.

Usage

mergeNiftis(inputPaths, direction = c("x", "y", "z", "t"), outfile = NULL,
  ncores = 1, ...)

Arguments

Value

Returns a merged NIfTI image

Create parametric maps

Description

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. You can select which type of p-value correction you want done on the map. The z-maps are signed just like FSL.

Usage

parMap(parameters, mask, method = "none", outDir = NULL)

Arguments

Value

Return 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 = runif(25))
fm1 <- "~ x + y"
models <- vlmParam(image=image, mask=mask, 
              formula=fm1, subjData=covs, ncores = 1)
Maps <- parMap(models, mask, method="fdr")

GAM plotting using ggplot2

Description

GAM plotting using ggplot2

Usage

plotGAM(gamFit, smooth.cov, groupCovs = NULL, orderedAsFactor = T,
  rawOrFitted = F, plotCI = T)

Arguments

Value

Returns a ggplot object that can be visualized using the print() function

See Also

Other Plotting: plotGAMM

Examples

data <- data.frame(x = rep(1:20, 2), group = rep(1:2, each = 20))
set.seed(1)
data$y <- (data$x^2)*data$group*3 + rnorm(40, sd = 200)
data$group <- ordered(data$group)

gam <- mgcv::gam(y ~ s(x) + group, data=data)

plot1 <- plotGAM(gamFit = gam, smooth.cov = "x", groupCovs = NULL,
                  rawOrFitted = "raw", plotCI=TRUE, orderedAsFactor = FALSE)
gam <- mgcv::gam(y ~ s(x) + group + s(x, by=group), data=data)
plot2 <- plotGAM(gamFit = gam, smooth.cov = "x", groupCovs = "group",
                             rawOrFitted = "raw", orderedAsFactor = FALSE)

GAMM plotting using ggplot2

Description

GAMM plotting using ggplot2

Usage

plotGAMM(gammFit, smooth.cov, groupCovs = NULL, orderedAsFactor = F,
  rawOrFitted = F, plotCI = T, grouping = NULL)

Arguments

Value

Returns a ggplot object that can be visualized using the print() function

See Also

Other Plotting: plotGAM

Examples

set.seed(1)
data <- data.frame(x = (seq(.25,25, .25) +rnorm(100)), group = rep(1:2, 5), z=rnorm(100),
              index.rnorm = rep(rnorm(50, sd = 50), 2), index.var = rep(1:50, 2))
data$y <- (data$x)*data$group*10 + rnorm(100, sd = 700) + data$index.rnorm + data$z
data$group <- ordered(data$group)


gamm <- gamm4::gamm4(y ~ + s(x) + s(x, by=group) + z + group, data=data, random = ~ (1|index.var))


plot <- plotGAMM(gammFit <- gamm, smooth.cov <- "x", groupCovs = "group",
                    plotCI <- T, rawOrFitted = "raw", grouping = "index.var")

plot2 <- plotGAMM(gammFit <- gamm, smooth.cov <- "x", groupCovs = "group",
                  plotCI <- T, rawOrFitted = "fitted", grouping = "index.var")

Run a Generalized Additive Model on all voxels of a NIfTI image and return coefficients and residuals

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.

Usage

rgamParam(image, mask, fourdOut = NULL, formula, subjData,
  mc.preschedule = TRUE, ncores = 1, ...)

Arguments

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))
fm1 <- "~ s(x)"
models <- rgamParam(image=image, mask=mask, 
              formula=fm1, subjData=covs, ncores = 1, method="REML")

Run a Generalized Additive Mixed Effects Model on all voxels of a NIfTI image return coefficients and residuals

Description

This function is able to run a Generalized Mixed Effects Model (GAMM) using the gamm4() function. The analysis will run in all voxels in in the mask and will return parametric and smooth coefficients.

Usage

rgamm4Param(image, mask, fourdOut = NULL, formula, randomFormula, subjData,
  mc.preschedule = TRUE, ncores = 1, ...)

Arguments

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(c(rep(0,15), 1), 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)"
models <- rgamm4Param(image, mask, formula = fm1, 
                 randomFormula = randomFormula, subjData = covs, ncores = 1, REML=TRUE)

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

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)

Run a Linear Mixed Effects Model on all voxels of a NIfTI image and return parametric coefficients and residuals

Description

This function is able to run a Linear Mixed Effect Model using the lmer() function. The analysis will run in all voxels in in the mask and will return the model fit for each voxel. The function relies on lmerTest to create p-values using the Satterthwaite Approximation.

Usage

rlmerParam(image, mask, fourdOut = NULL, formula, subjData,
  mc.preschedule = TRUE, ncores = 1, ...)

Arguments

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(c(rep(0,15),1), 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 <- rlmerParam(image, mask, formula = fm1, subjData = covs, ncores = 1)

Create parametric maps and residuals

Description

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.

Usage

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

Arguments

Value

Return 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 = 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)

Timeseries to Matrix

Description

This function is able to mask a 4-Dimensional image and create a matrix from it. Each column represents the same voxel in the xyz array while the rows represent the t-dimension.

Usage

ts2matrix(image, mask)

Arguments

Examples

image <- oro.nifti::nifti(img = array(1:64, dim =c(4,4,4,5)))
mask <- oro.nifti::nifti(img = array(0:1, dim = c(4,4,4)))
matrix <- ts2matrix(image, mask)

Timeseries to Mean Cluster

Description

This function is able to output the mean voxel intensity over a cluster. Each column represents a cluster and the rows represent the t-dimension.

Usage

ts2meanCluster(image, mask)

Arguments

Examples

image <- oro.nifti::nifti(img = array(1:320, dim =c(4,4,4,5)))
mask <- oro.nifti::nifti(img = array(0:15, dim = c(4,4,4,1)))
matrix <- ts2meanCluster(image, mask)

Run a Generalized Additive Model on all voxels of a NIfTI image within a mask and return parametric and smooth coefficients tables

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.

Usage

vgamParam(image, mask, fourdOut = NULL, formula, subjData,
  mc.preschedule = TRUE, ncores = 1, ...)

Arguments

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))
fm1 <- "~ s(x)"
models <- vgamParam(image=image, mask=mask, 
              formula=fm1, subjData=covs, ncores = 1, method="REML")

Run a Generalized Additive Mixed Effects Model on all voxels of a NIfTI image and return parametric and smooth coefficients

Description

This function is able to run a Generalized Mixed Effects Model (GAMM) using the gamm4() function. The analysis will run in all voxels in in the mask and will return parametric and smooth coefficients.

Usage

vgamm4Param(image, mask, fourdOut = NULL, formula, randomFormula, subjData,
  mc.preschedule = TRUE, ncores = 1, ...)

Arguments

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(c(rep(0,15), 1), 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)"
models <- vgamm4Param(image, mask, formula = fm1, 
                 randomFormula = randomFormula, subjData = covs, ncores = 1, REML=TRUE)

Run a Linear Model on all voxels of a NIfTI image within a mask and and return parametric coefficients tables

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

vlmParam(image, mask, fourdOut = NULL, formula, subjData,
  mc.preschedule = TRUE, ncores = 1, ...)

Arguments

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 <- vlmParam(image=image, mask=mask, formula=fm1, subjData=covs, ncores = 1)

Run a Linear Mixed Effects Model on all voxels of a NIfTI image within a mask and and return parametric coefficients tables

Description

This function is able to run a Linear Mixed Effect Model using the lmer() function. The analysis will run in all voxels in in the mask and will return the model fit for each voxel. The function relies on lmerTest to create p-values using the Satterthwaite Approximation.

Usage

vlmerParam(image, mask, fourdOut = NULL, formula, subjData,
  mc.preschedule = TRUE, ncores = 1, ...)

Arguments

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(c(rep(0,15),1), 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 <- vlmerParam(image, mask, formula = fm1, subjData = covs, ncores = 1)