[BioC] lower.limits and upper.limits in glmnet
Yue Li
yueli at cs.toronto.edu
Mon Oct 21 20:31:24 CEST 2013
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
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