knitr::opts_chunk$set(echo = TRUE)
Diagnose optimization issues with Lei's example
set.seed(777) devtools::load_all(".") X <- matrix(rnorm(1010 * 1000), 1010, 1000) beta <- rep(0, 1000) beta[1 : 200] <- 100 y <- X %*% beta + rnorm(1010) s = susie(X,y,L=1,estimate_residual_variance = TRUE) Y = y-s$Xr s2 = s$sigma2 x = seq(1,100000,length=100) l = rep(0,100) lg = rep(0,100) for(i in 1:100){ l[i] = loglik(x[i],Y,X,s2) lg[i] = loglik.grad(x[i],Y,X,s2) } plot(x,l) plot(x,lg) # > which.max(l) # [1] 23 # > lg[23] # [1] -2.398905e-07 # > lg[22] # [1] 6.282734e-07 lx = log(x) l2=l lg2=lg for(i in 1:100){ l2[i] = negloglik.logscale(lx[i],Y,X,s2) lg2[i] = negloglik.grad.logscale(lx[i],Y,X,s2) } plot(lx,l2) plot(lx,lg2) y = seq(0,20,length=100) l3=l2 lg3=lg2 for(i in 1:100){ l3[i] = negloglik.logscale(y[i],Y,X,s2) lg3[i] = negloglik.grad.logscale(y[i],Y,X,s2) } plot(y,l3) plot(y,lg3) uniroot(negloglik.grad.logscale,c(-20,20),extendInt = "upX",Y=Y,X=X,s2=s2)
So, to summarize, problem seems to be that optim has issues with very flat initial gradient near 0.
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