mild: Efficiently compute a multivariate, image-based (mixed)...

In neuroconductor-devel/ANTsR: ANTs in R: Quantification Tools for Biomedical Images

Efficiently compute a multivariate, image-based (mixed) linear decompositition (mild)

Description

This function simplifies calculating image-wide multivariate PCA maps. The model will minimize a matrix energy similar to norm( X - UVt - UranVrant ) where the U are standard design and random effect (intercept) design matrices. The random intercept matrix is only included if repeated measures are indicated.

Usage

mild(
  dataFrame,
  voxmats,
  basisK,
  myFormulaK,
  smoothingMatrix,
  iterations = 10,
  gamma = 1e-06,
  sparsenessQuantile = 0.5,
  positivity = c("positive", "negative", "either"),
  initializationStrategy = 0,
  repeatedMeasures = NA,
  orthogonalize = FALSE,
  verbose = FALSE
)

Arguments

dataFrame	This data frame contains all relevant predictors except for the matrices associated with the image variables.
voxmats	The named list of matrices that contains the changing predictors.
basisK	an integer determining the size of the basis.
myFormulaK	This is a character string that defines a valid regression which in this case should include predictors named as paste0("mildBasis",1:basisK)
smoothingMatrix	allows parameter smoothing, should be square and same size as input matrix
iterations	number of gradient descent iterations
gamma	step size for gradient descent
sparsenessQuantile	quantile to control sparseness - higher is sparser
positivity	restrict to positive or negative solution (beta) weights. choices are positive, negative or either as expressed as a string.
initializationStrategy	optional initialization matrix or seed. seed should be a single number; matrix should be a n by k matrix.
repeatedMeasures	list of repeated measurement identifiers. this will allow estimates of per identifier intercept.
orthogonalize	boolean to control whether we orthogonalize the v
verbose	boolean to control verbosity of output

Value

A list of different matrices that contain names derived from the formula and the coefficients of the regression model.

Author(s)

BB Avants.

See Also

milr

Examples

set.seed(1500)
nsub <- 12
npix <- 100
outcome <- rnorm(nsub)
covar <- rnorm(nsub)
mat <- replicate(npix, rnorm(nsub))
mat2 <- replicate(npix, rnorm(nsub))
nk <- 3
myform <- paste(
  " vox2 ~ covar + vox + ",
  paste0("mildBasis", 1:nk, collapse = "+")
) # optional covariates
df <- data.frame(outcome = outcome, covar = covar)
result <- mild(df, list(vox = mat, vox2 = mat2),
  basisK = 3, myform,
  initializationStrategy = 10
)
result <- mild(df, list(vox = mat, vox2 = mat2),
  basisK = 3, myform,
  initializationStrategy = 4
)
myumat <- svd(mat2, nv = 0, nu = 3)$u
result <- mild(df, list(vox = mat, vox2 = mat2),
  basisK = 3, myform,
  initializationStrategy = 0
)

neuroconductor-devel/ANTsR documentation built on July 3, 2025, 10:41 p.m.