My read of "help cvlasso" is that e(varXmodel) should contain only variables chosen by cvlasso. However, it keep returning all of the variables, despite only a subset being chosen. Example of issue below (meaningly empirical example). Has anyone experienced this? Alternatively, does anyone have another way to get the selected variables (to be used elsewhere as variables in a regression)?

Thank you!

CODE:
local coeffs "total_duration max_duration tmmx tmmn vs sph pr mvs_10m snwd_s snow_s awnd_s wsf2_s wsf5_s"

local conditions "fe nf(5) lc(150) seed(1234)"

local sv "lse"
local estsample_dum "year>2015&year<2021&rand<=.8"

cvlasso cmi_dum `coeffs', `conditions' `sv', if `estsample_dum'&rand<.1

RESULTS:
K-fold cross-validation with 5 folds. Elastic net with alpha=1.
Fold 1 2 3 4 5
| Lambda MSPE st. dev.
----------+---------------------------------------------
1| 5380.1722 .06756452 .00049734
...cut...
14| 2408.8119 .06663226 .00047482 ^
...cut...
88| 24.84424 .06617284 .00047316 *
...cut...
* lopt = the lambda that minimizes MSPE.
Run model: cvlasso, lopt
^ lse = largest lambda for which MSPE is within one standard error of the minimal MSPE.
Run model: cvlasso, lse
Estimate lasso with lambda=2408.812 (lse).

---------------------------------------------------
Selected | Lasso Post-est OLS
------------------+--------------------------------
max_duration | 0.0437135 0.0838706
vs | 0.0008427 0.0071744
sph | 1.5681662 4.9519355
wsf5_s | 0.0003178 0.0003982
---------------------------------------------------

. dis e(varXmodel)
total_duration max_duration tmmx tmmn vs sph pr mvs_10m snwd_s snow_s awnd_s wsf2_s wsf5_s

. dis e(varX)
total_duration max_duration tmmx tmmn vs sph pr mvs_10m snwd_s snow_s awnd_s wsf2_s wsf5_s

. dis e(partial)
.