model <- vglm(log_load_smoothed ~ treatment1 + treatment2,
              family = tobit(Lower = 0),
              data = clients[nonemail_holdout == 0])

Call:
vglm(formula = log_load_smoothed ~ treatment1 + treatment2, family = tobit(Lower = 0),
    data = clients[nonemail_holdout == 0])


Pearson residuals:
              Min       1Q   Median       3Q   Max
mu       -18.2011 -0.01683 -0.01514 -0.01482 19.83
loge(sd)  -0.0668 -0.06317 -0.05865 -0.05779 22.85

Coefficients:
                 Estimate Std. Error z value Pr(>|z|)
(Intercept):1   -36.39145    0.66122 -55.037  < 2e-16 ***
(Intercept):2     2.59319    0.01717 150.991  < 2e-16 ***
treatment1Save    0.38739    0.20983   1.846  0.06486 .
treatment1Offer   1.20873    0.20372   5.933 2.97e-09 ***
treatment2Save    0.05581    0.20620   0.271  0.78666
treatment2Offer   0.52358    0.20229   2.588  0.00965 **
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Number of linear predictors:  2

Names of linear predictors: mu, loge(sd)