Simple subgradientL1Regression function.

SubgradientL1Regression solves y approx x beta

subgradientL1Regression(
  y,
  x,
  s = 0.01,
  percentvals = 0.1,
  nits = 100,
  betas = NA,
  sparval = NA
)

Arguments

y

outcome variable
x

predictor matrix
s

gradient descent parameter
percentvals

percent of values to use each iteration
nits

number of iterations
betas

initial guess at solution
sparval

sparseness

Value

output has a list of summary items

Author

Avants BB

Examples


mat<-replicate(1000, rnorm(200))
y<-rnorm(200)
wmat<-subgradientL1Regression( y, mat, percentvals=0.05 )
print( wmat$resultcorr )
#>       cor 
#> 0.7333143