Truncated Negative Binomial Model Unexpected Marginal Means

Dear All,

Hope all is well! This may be a na?ve question but I am running a hurdle negative binomial model to look at the differences in counts of differing types in different locations. My major interest is the conditional model (ie when counts are above 0).

I run the following code:

model<-glmmTMB(Count ~ Location*Type + (1 | ID), zi=~Location*Type + (1|ID), data=data, family="truncated_nbinom1",control=glmmTMBControl(optimizer=optim, optArgs=list(method="BFGS")))

var.corr <-VarCorr(model)

Conditional model:
Groups Name        Std.Dev.
ID     (Intercept) 0.37105

Zero-inflation model:
Groups Name        Std.Dev.
ID     (Intercept) 1.3207

emmeans <- emmeans(model, ~ Location*Type, type="response", sigma=0.37105, bias.adjust=TRUE)

Location Type response    SE  df lower.CL upper.CL
0     0             1.117 0.277 631    0.687     1.82
1     0             0.940 0.251 631    0.556     1.59
2     0             0.893 0.266 631    0.498     1.60
0     1             1.325 0.254 631    0.909     1.93
1     1             1.090 0.248 631    0.698     1.70
2     1             1.452 0.300 631    0.967     2.18

Confidence level used: 0.95
Intervals are back-transformed from the log scale
Bias adjustment applied based on sigma = 0.37105

However, I?m not sure why the estimated means and confidence intervals will include values below 1 in the conditional model as I anticipated these values would represent the average number of non-zero counts? Is there something I may be doing wrong or not understanding?

Thanks in advance for your help!

Warm Regards,
Alex