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

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

smoothMultiRegression(
  x,
  y,
  iterations = 10,
  sparsenessQuantile = 0.5,
  positivity = FALSE,
  smoothingMatrixX = NA,
  smoothingMatrixY = NA,
  nv = 2,
  extraPredictors,
  verbose = FALSE
)

Arguments

x	input matrix on which prediction is based
y	target matrix
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.
smoothingMatrixX	allows parameter smoothing, should be square and same size as input matrix
smoothingMatrixY	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

Author

Avants BB

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 = matrix( rnorm( nrow(mat)*2 ), ncol=2 )
for ( i in c( 5000:6000, 10000:11000, 16000:17000 )  ){
  mat[ , i ] = age[,1]*0.1 + mat[,i]
  }
sel = 1:25
fit = smoothMultiRegression( x=mat[sel,], y=age[sel,], iterations = 10,
  sparsenessQuantile = 0.5,
  smoothingMatrixX = smoomat, smoothingMatrixY = NA, verbose=T )
tt = mat %*% fit$v
print( cor.test( age[-sel,1], tt[-sel,1] ) )
vimg = makeImage( mask, (fit$v[,1] ) ); print(range(vimg)*10)
plot( mask, vimg, window.overlay=range(abs(vimg)))
}