Description Usage Arguments Value Author(s) Examples
View source: R/multiscaleSVDxpts.R
Reconstruct a n by 1 vector given n by p matrix of predictors.
1 2 3 4 5 6 7 8 9 10 11 | smoothRegression(
x,
y,
iterations = 10,
sparsenessQuantile = 0.5,
positivity = FALSE,
smoothingMatrix = NA,
nv = 2,
extraPredictors,
verbose = FALSE
)
|
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 |
vector of size p is output
Avants BB
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | ## Not run:
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)))
## End(Not run)
|
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