glmnet.lasso_maxCoef(): incorrect coefficient sorting? [bug]

[ptaitAtMcMaster]

Hi,
on line 129 of fitfuns.R, it looks like glmnet.lasso_maxCoef() is not sorting the coefficients in the right order. The code is:
selected <- order(coef(fit)[-1])[1:q]

Here is a reproducible example that would capture lines 126 and 129:

library(glmnet)
library(stabs)
set.seed(36)

data(QuickStartExample)
x <- QuickStartExample$x
y <- QuickStartExample$y

lambda.1se <- cv.glmnet(x, y)$lambda.1se # 0.1322761

fit <- glmnet(x, y, lambda = lambda.1se)
fit$df # 8

## coefficients minus the intercept
coef_vec <- coef(fit)[-1] 
coef_vec
# 1.3019591  0.0000000  0.6422423  0.0000000 -0.7892393  0.4944803  0.0000000  0.2943201  0.0000000  0.0000000  0.1058430
# 0.0000000  0.0000000 -1.0402308  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000 -0.9791165

##  line 129 :: order(coef(fit)[-1])[1:q]
i1 <- order(coef_vec); i1
# [1] 14 20  5  2  4  7  9 10 12 13 15 16 17 18 19 11  8  6  3  1
q <- 9; coef_vec[i1[1:q]]
# -1.0402308 -0.9791165 -0.7892393  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000

I would have expected the sorting to be done on the absolute value of the coefficients. Assuming this is the intent, two possible solutions are:

i2 <- order(abs(coef_vec), decreasing = TRUE); i2
# [1]  1 14 20  5  3  6  8 11  2  4  7  9 10 12 13 15 16 17 18 19
coef_vec[i2[1:q]]
# 1.3019591 -1.0402308 -0.9791165 -0.7892393  0.6422423  0.4944803  0.2943201  0.1058430  0.0000000

or

selected <- predict(fit, type = "nonzero")
selected <- selected[[length(selected)]] ## length is 1 when lambda is a scalar
selected
#  1  3  5  6  8 11 14 20

I believe the later solution is used in glmnet.lasso() on lines 31 and 32 of fitfuns.R

Thanks
Peter

## verify that the same coefficients are chosen 
all(selected %in% i2[1:fit$df])
# TRUE