View source: R/multiscaleSVDxpts.R
mild | R Documentation |
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.
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.
milr
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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