Description Usage Arguments Details Value Author(s) References Examples
View source: R/lassoshooting.R
Efficient estimates of sparse regression coefficients with a lasso (L1) penalty
1 
X 
Design matrix: N by p matrix of p explanatory variables 
y 
vector of 1 response variable for N observations 
XtX 
X'X, could be given together with X'y instead of X and y 
Xty 
X'y, could be given together with X'X instead of X and y 
lambda 
(Nonnegative) regularization parameter for lasso. lambda=0 means no regularization. 
thr 
Threshold for convergence. Default value is 1e4. Iterations stop when max absolute parameter change is less than thr 
maxit 
Maximum number of iterations of outer loop. Default 10,000 
nopenalize 
List of coefficients not to penalize starting at 0 
penaltyweight 
p weights, one per variable, will be multiplied by overall lambda penalty 
trace 
Level of detail for printing out information as iterations proceed. Default 0 – no information 
... 
Reserved for experimental options 
Estimates a sparse regression coefficient vector using a lasso (L1) penalty using the approach of cyclic coordinate descent. See references for details.
The solver does NOT include an intercept, add a column of ones to x
if your data is not centered.
A list with components
coefficients 
Estimated regression coefficient vector 
iterations 
Number of iterations of outer loop used by algorithm 
delta 
Change in parameter value at convergence 
infnorm 
X'y_∞ 
Tobias Abenius
Rebecka Jörnsten, Tobias Abenius, Teresia Kling, Linnéa Schmidt, Erik Johansson, Torbjörn Nordling, Bodil Nordlander, Chris Sander, Peter Gennemark, Keiko Funa, Björn Nilsson, Linda Lindahl, Sven Nelander. (2011) Network modeling of the transcriptional effects of copy number aberrations in glioblastoma. Molecular Systems Biology 7 (to appear)
Friedman J, Hastie T, et al. (2007) Pathwise coordinate optimization. Ann Appl Stat 1: 302–332
Fu WJ (1998) Penalized regressions: the bridge versus the lasso. J Comput Graph Statist 7: 397–416
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30  ## Not run:
set.seed(42)
b < seq(3,3,length=10)
n<100;
p<10;
X < matrix(rnorm(n*p),n,p)
noise < as.matrix(rnorm(n,sd=0.1))
y < X
require(lassoshooting)
# FIXME: write proper example using R built in dataset
#add intercept column to the design matrix
Xdesign < cbind(1,X)
lambda < 20
#don't penalize the intercept
bhat < lassoshooting(X=Xdesign,y=y,lambda=lambda,nopenalize=0)
#above equals below
bhat1 < lassoshooting(X=Xdesign,y=y,lambda=2*lambda,penaltyweight=c(0,seq(0.5,0.5,length=p1)))
T1 < all(abs(bhat1bhat) < 1e20)
c < 10
bhat2 < lassoshooting(X=Xdesign,y=y, lambda=lambda, penaltyweight=c(0,1,1,1,1,1,c,c,c,c,c))
T2 < all(bhat2[2:6] > bhat2[7:11])
T1 && T2
## End(Not run)

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