# On the robustness of LASSO

Probably the last post on lasso, before the summer break… More specifically, I was wondering about the interpretation of graphs $\lambda\mapsto\widehat{\beta}_\lambda$. We use them for variable selection, but my major concern was about confidence intervals : how can we trust those lines ?

As usual, a natural way is to use simulations on generated datasets. Consider for instance

Sigma = matrix(c(1,.8,.2,.8,1,.4,.2,.4,1),3,3) n = 1000 library(mnormt) X = rmnorm(n,rep(0,3),Sigma) set.seed(123) df = data.frame(X1=X[,1],X2=X[,2],X3=X[,3],X4=rnorm(n), X5=runif(n), X6=exp(X[,3]), X7=sample(c("A","B"),size=n,replace=TRUE,prob=c(.5,.5)), X8=sample(c("C","D"),size=n,replace=TRUE,prob=c(.5,.5))) df$Y = 1+df$X1-df$X4+5*(df$X7=="A")+rnorm(n)

One can use other simulations of datasets, and store the output

vlambda = exp(seq(-8,1,length=201)) lasso = glmnet(x=X,y=df[,"Y"],family="gaussian",alpha=1, lambda=vlambda,standardize=TRUE) VLASSO[[s]] = as.matrix(lasso$beta) To visualize confidence bands, one can compute quantiles Q05=Q95=Qm=matrix(NA,9,201) for(i in 1:nrow(Q05)){ for(j in 1:ncol(Q05)){ v = unlist(lapply(VLASSO,function(x) x[i,j])) Q05[i,j] = quantile(v,.05) Q95[i,j] = quantile(v,.95) Qm[i,j] = mean(v) }} and get get the graph plot(lasso,col=colrs,"lambda"ylim=c(min(Q05),max(Q95))) colrs=c(brewer.pal(8,"Set1")) polygon(c(log(lasso$lambda),rev(log(lasso$lambda))), c(Q05[2,],rev(Q95[2,])),col=colrs[1],border=NA) polygon(c(log(lasso$lambda),rev(log(lasso$lambda))), c(Q05[5,],rev(Q95[5,])),col=colrs[2],border=NA) polygon(c(log(lasso$lambda),rev(log(lasso$lambda))), c(Q05[8,],rev(Q95[8,])),col=colrs[3],border=NA) An alternative (more realistic on real data) is to use bootstrapped version of the dataset id = sample(1:nrow(X),size=nrow(X),replace=TRUE) lasso = glmnet(x=X[id,],y=df[id,"Y"],family="gaussian",alpha=1, lambda=vlambda,standardize=TRUE) So far, it looks it’s working very well. Now, what if we have a smaller dataset n = 100 On simulated new samples, we get while the bootstrap version is There is more uncertainty, clearly, but the conclusion is not ambiguous here. Now, what about real data. Consider the following chicago = read.table("http://freakonometrics.free.fr/chicago.txt",header=TRUE,sep=";") tail(chicago) Fire X_1 X_2 X_3 42 4.8 0.152 19 13.323 43 10.4 0.408 25 12.960 44 15.6 0.578 28 11.260 45 7.0 0.114 3 10.080 46 7.1 0.492 23 11.428 47 4.9 0.466 27 13.731 with one variable of interest (the number of fires, per unhabitants) and 3 features. We can here use bootstrap to generate samples, and then fit a lasso regression. On the original dataset, the regression is X = model.matrix(lm(Fire~.,data=chicago)) id = sample(1:nrow(X),size=nrow(X),replace=TRUE) vlambda = exp(seq(-4,2,length=201)) lasso = glmnet(x=X[id,],y=chicago[id,"Fire"],family="gaussian",alpha=1, lambda=vlambda,standardize=TRUE) And if we just plot lines $\lambda\mapsto\widehat{\beta}_\lambda$ we get Now, consider bootstrap samples. for(s in 1:100){ id=sample(1:nrow(X),size=nrow(X),replace=TRUE) library(glmnet) vlambda=exp(seq(-4,2,length=201)) lasso=glmnet(x=X[id,],y=chicago[id,"Fire"],family="gaussian",alpha=1, lambda=vlambda,standardize=TRUE) plot(lasso,col=colrs,"lambda",lwd=.2,add=TRUE)} We get here The interpretation here is much more difficult What about the order ? N=matrix(NA,100000,4) for(s in 1:100000){ id=sample(1:nrow(X),size=nrow(X),replace=TRUE) library(glmnet) vlambda=exp(seq(-4,2,length=201)) lasso=glmnet(x=X[id,],y=chicago[id,"Fire"], family="gaussian",alpha=1, lambda=vlambda,standardize=TRUE) N[s,]=names(sort(apply(as.matrix(lasso$beta), 1,function(x) sum(x!=0))))}

The ordering that was obtained on the original dataset was the same in 56% of the scenarios,

mean(apply(N,1,function(x) paste(x,collapse="")=="(Intercept)X_1X_2X_3")) [1] 0.5693

We can look at all the cases,

L=as.character(c(123,132,213,231,312,321)) Li=paste("(Intercept)X_",substr(L,1,1),"X_", substr(L,2,2),"X_",substr(L,3,3),sep="") g=function(y) mean(apply(N,1,function(x) paste(x,collapse="")==y)) vL=unlist(lapply(Li,g)) names(vL)=L barplot(vL,las=2,horiz=TRUE)

This site uses Akismet to reduce spam. Learn how your comment data is processed.