Convert a matrix to a form that can be used to initialize sparse cca and pca.

InitializeEigenanatomy is a helper function to initialize sparseDecom and sparseDecom2. Can be used to estimate sparseness parameters per eigenvector. The user then only chooses nvecs and optional regularization parameters.

initializeEigenanatomy(initmat, mask = NULL, nreps = 1, smoothing = 0)

Arguments

initmat	input matrix where rows provide initial vector values. alternatively, this can be an antsImage which contains labeled regions.
mask	mask if available
nreps	nrepetitions to use
smoothing	if using an initial label image, optionally smooth each roi

Value

list is output

Examples

mat<-t(replicate(3, rnorm(100)) )
initdf<-initializeEigenanatomy( mat ) # produces a mask
dmat<-replicate(100, rnorm(20)) # data matrix
svdv = t( svd( mat, nu=0, nv=10 )$v )
ilist = matrixToImages( svdv, initdf$mask )
eseg = eigSeg( initdf$mask, ilist,  TRUE  )
eanat<-sparseDecom( dmat, inmask=initdf$mask,
 sparseness=0, smooth=0,
 initializationList=ilist, cthresh=0,
 nvecs=length(ilist) )
initdf2<-initializeEigenanatomy( mat, nreps=2 )
eanat<-sparseDecom( dmat, inmask=initdf$mask,
  sparseness=0, smooth=0, z=-0.5,
  initializationList=initdf2$initlist, cthresh=0,
  nvecs=length(initdf2$initlist) )
# now a regression
eanatMatrix<-eanat$eigenanatomyimages
# 'averages' loosely speaking anyway
myEigenanatomyRegionAverages<-dmat %*% t( eanatMatrix )
dependentvariable<-rnorm( nrow(dmat) )
summary(lm( dependentvariable ~ myEigenanatomyRegionAverages ))
#> 
#> Call:
#> lm(formula = dependentvariable ~ myEigenanatomyRegionAverages)
#> 
#> Residuals:
#>     Min      1Q  Median      3Q     Max 
#> -1.5471 -0.7817  0.1856  0.7615  1.2461 
#> 
#> Coefficients: (1 not defined because of singularities)
#>                                Estimate Std. Error t value Pr(>|t|)
#> (Intercept)                   -0.005678   0.256690  -0.022    0.983
#> myEigenanatomyRegionAverages1 -0.733521   2.057987  -0.356    0.727
#> myEigenanatomyRegionAverages2  0.558452   1.969882   0.283    0.781
#> myEigenanatomyRegionAverages3 -2.957976  14.121203  -0.209    0.837
#> myEigenanatomyRegionAverages4  3.282327  14.377400   0.228    0.823
#> myEigenanatomyRegionAverages5        NA         NA      NA       NA
#> myEigenanatomyRegionAverages6 -0.408361   0.482746  -0.846    0.412
#> 
#> Residual standard error: 1.056 on 14 degrees of freedom
#> Multiple R-squared:  0.09468,	Adjusted R-squared:  -0.2287 
#> F-statistic: 0.2928 on 5 and 14 DF,  p-value: 0.909
#> 

nvox<-1000
dmat<-replicate(nvox, rnorm(20))
dmat2<-replicate(30, rnorm(20))
mat<-t(replicate(3, rnorm(nvox)) )
initdf<-initializeEigenanatomy( mat )
eanat<-sparseDecom2(
 inmatrix = list(dmat,dmat2),
 inmask=list(initdf$mask,NA),
  sparseness=c( -0.1, -0.2 ),
  smooth=0,
  initializationList=initdf$initlist,
  cthresh=c(0,0),
  nvecs=length(initdf$initlist), priorWeight = 0.1 )