| Title: | Mass-Univariate Voxelwise Analysis of Medical Imaging Data |
|---|---|
| Description: | Functions for the mass-univariate voxelwise analysis of medical imaging data that follows the NIfTI <http://nifti.nimh.nih.gov> format. |
| Authors: | Angel Garcia de la Garza [aut, cre], Simon Vandekar [aut], David Roalf [aut], Kosha Ruparel [aut], Ruben Gur [aut], Raquel Gur [aut], Theodore Satterthwaite [aut], R. Taki Shinohara [aut] |
| Maintainer: | Angel Garcia de la Garza <[email protected]> |
| License: | GPL-2 |
| Version: | 1.3.5 |
| Built: | 2024-11-24 03:24:34 UTC |
| Source: | https://github.com/angelgar/voxel |
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.
anovagammVoxel(image, mask, fourdOut = NULL, formula, randomFormula, subjData, dispersion = NULL, freq = FALSE, mc.preschedule = TRUE, ncores = 1, ...)anovagammVoxel(image, mask, fourdOut = NULL, formula, randomFormula, subjData, dispersion = NULL, freq = FALSE, mc.preschedule = TRUE, ncores = 1, ...)
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. |
fourdOut |
To be passed to mergeNifti, This is the output path to write out the fourd file. Do not include a suffix (i.e. .nii.gz). 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 |
dispersion |
To be passed to mgcv::anova.gam, Defaults to NULL. Dispersion Parameter, not normally used. |
freq |
To be passed to mgcv::anova.gam, Defaults to FALSE. Frequentist or Bayesian approximations for p-values |
mc.preschedule |
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 gamm4() |
Returns list of models fitted to each voxel over the masked images passed to function.
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)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)
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.
anovagamVoxel(image, mask, fourdOut = NULL, formula, subjData, dispersion = NULL, freq = FALSE, mc.preschedule = TRUE, ncores = 1, ...)anovagamVoxel(image, mask, fourdOut = NULL, formula, subjData, dispersion = NULL, freq = FALSE, mc.preschedule = TRUE, ncores = 1, ...)
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 |
dispersion |
To be passed to mgcv::anova.gam, Defaults to NULL. Dispersion Parameter, not normally used. |
freq |
To be passed to mgcv::anova.gam, Defaults to FALSE. Frequentist or Bayesian approximations for p-values |
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 gam() |
Returns list of models fitted to each voxel over the masked images passed to function.
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)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)
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.
anovalmerVoxel(image, mask, fourdOut = NULL, formula, subjData, ddf = "Satterthwaite", type = 3, mc.preschedule = TRUE, ncores = 1, ...)anovalmerVoxel(image, mask, fourdOut = NULL, formula, subjData, ddf = "Satterthwaite", type = 3, mc.preschedule = TRUE, ncores = 1, ...)
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 lmer() |
subjData |
Dataframe containing all the covariates used for the analysis |
ddf |
Which approximation of DDF to be used. To be passed to anova.lmerModLmerTest. Defaults to "Satterthwaite" |
type |
Type of hypothesis to be test (defined from SAS terminology). Defaults to 3. To be passed to anova.lmerModLmerTest |
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() |
Returns list of models fitted to each voxel over the masked images passed to function.
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)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)
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.
anovalmVoxel(image, mask, fourdOut = NULL, formula, subjData, mc.preschedule = TRUE, ncores = 1, ...)anovalmVoxel(image, mask, fourdOut = NULL, formula, subjData, mc.preschedule = TRUE, ncores = 1, ...)
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 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 lm() |
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 lm() |
Returns list of models fitted to each voxel over the masked images passed to function.
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)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)
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.
gamCluster(image, mask, fourdOut = NULL, formula, subjData, mc.preschedule = TRUE, ncores = 1, ...)gamCluster(image, mask, fourdOut = NULL, formula, subjData, mc.preschedule = TRUE, ncores = 1, ...)
image |
Input image of type 'nifti' or vector of path(s) to images. If multiple paths, the script will call mergeNiftis() 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 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 for the analysis |
... |
Additional arguments passed to gam() |
Returns list of models fitted to the mean voxel intensity over region of interest.
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")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")
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.
gammCluster(image, mask, fourdOut = NULL, formula, randomFormula, subjData, mc.preschedule = TRUE, ncores = 1, ...)gammCluster(image, mask, fourdOut = NULL, formula, randomFormula, subjData, mc.preschedule = TRUE, ncores = 1, ...)
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 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 for the analysis |
... |
Additional arguments passed to gamm4() |
Returns list of models fitted to the mean voxel intensity a region or interest.
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)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)
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
gammNIfTI(image, mask, fourdOut = NULL, formula, randomFormula, subjData, mc.preschedule = TRUE, ncores = 1, method = "none", residual = FALSE, outDir = NULL, ...)gammNIfTI(image, mask, fourdOut = NULL, formula, randomFormula, subjData, mc.preschedule = TRUE, ncores = 1, method = "none", residual = FALSE, outDir = NULL, ...)
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() |
Returns Parametric maps of the fitted models over the NIfTI image
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)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)
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.
gamNIfTI(image, mask, fourdOut = NULL, formula, subjData, mc.preschedule = TRUE, ncores = 1, method = "none", residual = FALSE, outDir = NULL, ...)gamNIfTI(image, mask, fourdOut = NULL, formula, subjData, mc.preschedule = TRUE, ncores = 1, method = "none", residual = FALSE, outDir = NULL, ...)
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() |
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 = rnorm(25)) fm1 <- "~ x + s(y)" Maps <- gamNIfTI(image=image, mask=mask, formula=fm1, subjData=covs, ncores = 1, method="fdr")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")
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)
gamRandomise(image, maskPath = NULL, formulaFull, formulaRed, subjData, outDir, nsim = 500, thresh = 0.01, run = FALSE)gamRandomise(image, maskPath = NULL, formulaFull, formulaRed, subjData, outDir, nsim = 500, thresh = 0.01, run = FALSE)
image |
Input path of 'nifti' image or vector of path(s) to images. If multiple paths, the script will all mergeNiftis() and merge across time. |
maskPath |
to mask. Must be a binary mask |
formulaFull |
Must be the formula of the full model (i.e. "~s(age,k=5)+sex+mprage_antsCT_vol_TBV") |
formulaRed |
Must be the formula of the reduced model (i.e. "~sex+mprage_antsCT_vol_TBV") |
subjData |
Dataframe containing all the covariates used for the analysis |
outDir |
output directory for randomise |
nsim |
Number of simulations |
thresh |
significance threshold |
run |
FALSE will only print randomise command but won't it |
Return randomise command
## 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)## 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)
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.
lmCluster(image, mask, fourdOut = NULL, formula, subjData, mc.preschedule = TRUE, ncores = 1, ...)lmCluster(image, mask, fourdOut = NULL, formula, subjData, mc.preschedule = TRUE, ncores = 1, ...)
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 lm() |
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 lm() |
Returns list of models fitted to the mean voxel intensity a region or interest.
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)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)
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.
lmerCluster(image, mask, fourdOut = NULL, formula, subjData, mc.preschedule = TRUE, ncores = 1, ...)lmerCluster(image, mask, fourdOut = NULL, formula, subjData, mc.preschedule = TRUE, ncores = 1, ...)
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() |
Returns list of models fitted to the mean voxel intensity a region or interest.
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)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)
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.
lmerNIfTI(image, mask, fourdOut = NULL, formula, subjData, mc.preschedule = TRUE, ncores = 1, method = "none", residual = FALSE, outDir = NULL, ...)lmerNIfTI(image, mask, fourdOut = NULL, formula, subjData, mc.preschedule = TRUE, ncores = 1, method = "none", residual = FALSE, outDir = NULL, ...)
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 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 |
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 lmer() |
Returns 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(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)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)
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.
lmNIfTI(image, mask, fourdOut = NULL, formula, subjData, mc.preschedule = TRUE, ncores = 1, method = "none", residual = FALSE, outDir = NULL, ...)lmNIfTI(image, mask, fourdOut = NULL, formula, subjData, mc.preschedule = TRUE, ncores = 1, method = "none", residual = FALSE, outDir = NULL, ...)
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 lm() |
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 lm() |
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" Maps <- lmNIfTI(image=image, mask=mask, formula=fm1, subjData=covs, ncores = 1, method="fdr")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")
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.
parMap(parameters, mask, method = "none", outDir = NULL)parMap(parameters, mask, method = "none", outDir = NULL)
parameters |
list of parametric and smooth table coefficients or ANOVA (like the output from vlmParam, vgamParam, anovalmVoxel) |
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) |
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 <- vlmParam(image=image, mask=mask, formula=fm1, subjData=covs, ncores = 1) Maps <- parMap(models, mask, method="fdr")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
plotGAM(gamFit, smooth.cov, groupCovs = NULL, orderedAsFactor = T, rawOrFitted = F, plotCI = T)plotGAM(gamFit, smooth.cov, groupCovs = NULL, orderedAsFactor = T, rawOrFitted = F, plotCI = T)
gamFit |
fitted gam model as produced by mgcv::gam() |
smooth.cov |
(character) name of smooth term to be plotted |
groupCovs |
(character) name of group variable to plot by, if NULL (default) then there are no groups in plot |
orderedAsFactor |
if TRUE then the model is refitted with ordered variables as factors. |
rawOrFitted |
If FALSE (default) then only smooth terms are plotted; if rawOrFitted = "raw" then raw values are plotted against smooth; if rawOrFitted = "fitted" then fitted values are plotted against smooth |
plotCI |
if TRUE (default) upper and lower confidence intervals are added at 2 standard errors above and below the mean |
Returns a ggplot object that can be visualized using the print() function
Other Plotting: plotGAMM
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$group) gam <- mgcv::gam(y ~ s(x) + group, data=data) plot1 <- plotGAM(gamFit = gam, smooth.cov = "x", groupCovs = NULL, rawOrFitted = "raw", plotCI=TRUE, orderedAsFactor = FALSE) gam <- mgcv::gam(y ~ s(x) + group + s(x, by=group), data=data) plot2 <- plotGAM(gamFit = gam, smooth.cov = "x", groupCovs = "group", rawOrFitted = "raw", orderedAsFactor = FALSE)data <- data.frame(x = rep(1:20, 2), group = rep(1:2, each = 20)) set.seed(1) data$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
plotGAMM(gammFit, smooth.cov, groupCovs = NULL, orderedAsFactor = F, rawOrFitted = F, plotCI = T, grouping = NULL)plotGAMM(gammFit, smooth.cov, groupCovs = NULL, orderedAsFactor = F, rawOrFitted = F, plotCI = T, grouping = NULL)
gammFit |
fitted gam model as produced by gamm4::gamm() |
smooth.cov |
(character) name of smooth term to be plotted |
groupCovs |
(character) name of group variable to plot by, if NULL (default) then there are no groups in plot |
orderedAsFactor |
Disabled |
rawOrFitted |
If FALSE (default) then only smooth terms are plotted; if rawOrFitted = "raw" then raw values are plotted against smooth; if rawOrFitted = "fitted" then fitted values are plotted against smooth |
plotCI |
if TRUE (default) upper and lower confidence intervals are added at 2 standard errors above and below the mean |
grouping |
(character) Name of variable that you want to use as the group argument in ggplot2::aes(), useful for better visualization of longitudinal data, (default is NULL) |
Returns a ggplot object that can be visualized using the print() function
Other Plotting: plotGAM
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$group) gamm <- gamm4::gamm4(y ~ + s(x) + s(x, by=group) + z + group, data=data, random = ~ (1|index.var)) plot <- plotGAMM(gammFit <- gamm, smooth.cov <- "x", groupCovs = "group", plotCI <- T, rawOrFitted = "raw", grouping = "index.var") plot2 <- plotGAMM(gammFit <- gamm, smooth.cov <- "x", groupCovs = "group", plotCI <- T, rawOrFitted = "fitted", grouping = "index.var")set.seed(1) data <- data.frame(x = (seq(.25,25, .25) +rnorm(100)), group = rep(1:2, 5), z=rnorm(100), index.rnorm = rep(rnorm(50, sd = 50), 2), index.var = rep(1:50, 2)) data$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")