Nothing

```
context("Consistency of penscale (vs theoretical and 'glmnet')")
test_that("weighted_ quad2theo", {
## SIMPLE CHECK: used identity for design matrix
n <- 100
p <- 100
x <- diag(rep(1,n))
y <- rnorm(100)
## no penscale...
lasso.quad <- elastic.net(x, y, intercept=FALSE, lambda2=0)
theo.path <- t(sapply(lasso.quad@lambda1, function(lambda) y*pmax(0,1-lambda/abs(y))))
expect_that(as.matrix(lasso.quad@coefficients), is_equivalent_to(theo.path))
## glmnet is not equal to what is expected... probably due to the intercept treatment
## lasso.glmn <- glmnet(x,y, intercept=FALSE,lambda.min.ratio=1e-2, thresh=1e-20)
## theo.path <- t(sapply(lasso.glmn$lambda*sqrt(n), function(lambda) y*pmax(0,1-lambda/abs(y))))
## expect_that(as.matrix(t(lasso.glmn$beta)), is_equivalent_to(theo.path))
## random penscale...
w <- 1/runif(p,0.5,1)
w <- w/sum(w)*p ## to fit glmnet rescaling
lasso.quad <- elastic.net(x, y,intercept=FALSE, penscale = w, lambda2=0)
theo.path <- t(sapply(lasso.quad@lambda1, function(lambda) y*pmax(0,1-lambda*w/abs(y))))
expect_that(as.matrix(lasso.quad@coefficients), is_equivalent_to(theo.path))
## glmnet with intercept fit with quadrupen and the theory
w <- 1/runif(p,0.5,1)
w <- w/sum(w)*p ## to fit glmnet rescaling
lasso.glmn <- glmnet(x,y, penalty.factor=w,lambda.min.ratio=1e-2, thresh=1e-20)
lasso.quad <- elastic.net(x,y, lambda1=lasso.glmn$lambda*sqrt(n), penscale = w, lambda2=0)
expect_that(as.matrix(t(lasso.glmn$beta)), is_equivalent_to(as.matrix(lasso.quad@coefficients)))
## Check the intercept term also
expect_that(lasso.glmn$a0, is_equivalent_to(lasso.quad@mu))
})
```

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