Description Usage Arguments Value Author(s) See Also Examples
View source: R/multiscaleSVDxpts.R
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.
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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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | 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 )
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