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
Examples
mat <- replicate(1000, rnorm(200))
y <- rnorm(200)
wmat <- subgradientL1Regression(y, mat, percentvals = 0.05)
print(wmat$resultcorr)
#> cor
#> 0.7699053