smooth matrix-based regression

Reconstruct a n by 1 vector given n by p matrix of predictors.

smoothRegression(
  x,
  y,
  iterations = 10,
  sparsenessQuantile = 0.5,
  positivity = FALSE,
  smoothingMatrix = NA,
  nv = 2,
  extraPredictors,
  verbose = FALSE
)

Arguments

x	input matrix on which prediction is based
y	target vector
iterations	number of gradient descent iterations
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.
smoothingMatrix	allows parameter smoothing, should be square and same size as input matrix
nv	number of predictor spatial vectors
extraPredictors	additional column predictors
verbose	boolean option

Value

vector of size p is output

Examples

if (FALSE) {
mask = getMask( antsImageRead( getANTsRData( 'r16' ) ) )
spatmat = t( imageDomainToSpatialMatrix( mask, mask ) )
smoomat = knnSmoothingMatrix( spatmat, k = 200, sigma = 1.0 )
mat <- matrix(rnorm(sum(mask)*50),ncol=sum(mask),nrow=50)
mat[ 1:25,100:10000]=mat[ 1:25,100:10000]+1
age = rnorm( 1:nrow(mat))
for ( i in c( 5000:6000, 10000:11000, 16000:17000 )  ){
  mat[ , i ] = age*0.1 + mat[,i]
  }
sel = 1:25
fit = smoothRegression( x=mat[sel,], y=age[sel], iterations = 10,
  sparsenessQuantile = 0.5,
  smoothingMatrix = smoomat, verbose=T )
tt = mat %*% fit$v
print( cor.test( age[-sel], tt[-sel,1] ) )
vimg = makeImage( mask, (fit$v[,1] ) ); print(range(vimg)*10)
plot( mask, vimg, window.overlay=range(abs(vimg)))
}