mild.RdThis 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.
mild( dataFrame, voxmats, basisK, myFormulaK, smoothingMatrix, iterations = 10, gamma = 0.000001, sparsenessQuantile = 0.5, positivity = c("positive", "negative", "either"), initializationStrategy = 0, repeatedMeasures = NA, orthogonalize = FALSE, verbose = FALSE )
| 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 |
| 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 |
A list of different matrices that contain names derived from the formula and the coefficients of the regression model.
BB Avants.
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 )