Hi Steve, Thanks for the reply. Yes, I tried it and it does seem to do that. I was just uncertain about how is achieved. Thanks anyways. Input: mycoef1 <- as.matrix(coef(glmnet(x,y,alpha=1,intercept=F),s=0.01)) mycoef2 <- as.matrix(coef(glmnet(x,y,alpha=1,intercept=F,upper.limits= c(rep(Inf,5), rep(0,15))),s=0.01)) print(cbind(mycoef1, mycoef2)) Output: 1 1 (Intercept) 0.000000000 0.000000000 V1 0.073877294 0.066218767 V2 0.034539506 0.004229772 V3 0.056682635 0.061373751 V4 -0.048408058 -0.067313603 V5 0.000000000 0.001866883 V6 -0.027033061 -0.024216745 V7 -0.053746653 -0.048835781 V8 0.151853999 0.000000000 V9 0.006023245 0.000000000 V10 0.007404765 0.000000000 V11 -0.087274939 -0.129084080 V12 0.019070975 0.000000000 V13 -0.031480104 -0.024312149 V14 0.075601445 0.000000000 V15 -0.021138293 -0.015392988 V16 0.013038938 0.000000000 V17 -0.046345120 -0.043179775 V18 -0.166853254 -0.158282024 V19 -0.051904691 -0.025384641 V20 -0.008883316 0.000000000 On 2013-10-21, at 2:13 PM, Steve Lianoglou <lianoglou.steve at="" gene.com=""> wrote: > Hi, > > On Mon, Oct 21, 2013 at 10:51 AM, Yue Li <yueli at="" cs.toronto.edu=""> wrote: >> Dear List, >> >> A quick question about using the glmnet package (which I know is not a BioC package ... apologies): >> >> Would the options "upper.limits" and "lower.limits" in glmnet be equivalent to an additionally constrained optimization on the range of the coefficients? >> >> For instance, would setting upper.limits to 0 be equivalent to a non-positive least squared linear regression (regularized)? > > Reading the documentation for those parameters in the `?glmnet` docs > certainly suggests so, no? > > This is simple enough for you to try yourself, though, so why not just > give it a shot and report back with your results? > > -steve > > -- > Steve Lianoglou > Computational Biologist > Bioinformatics and Computational Biology > Genentech