Dear list,

I'm analyzing the effect of a construction site (wea1, categorical variable
with 2 levels) on the number of birds (nbird). My model is:

*mod <- glmmadmb(nbird ~ wea1 + offset(log(size)) + (1|day_ID),
zeroInflation=TRUE, family="nbinom", data=buff_2000)*

where

*nbird:* number of birds (integer); includes many, many zeros

*wea1:* 0=before, 1=after (factor)

*area:* size of the surveyed area in which* n* birds were counted (numeric)

*day_ID*: suvey days (factor)

*buf_2000:* the full data set can be found at the end of this email

The ouput is:

>* summary(mod)*

Call:
glmmadmb(formula = nbird ~ wea1 + offset(log(area)) +  (1 | day_ID), data =
buff_2000, family = "nbinom", zeroInflation = TRUE)

AIC: 435.9

Coefficients:
                  Estimate  Std. Error z value     Pr(>|z|)
(Intercept)    1.748      0.475        3.68        0.00023 ***
wea11         -1.137      0.524       -2.17       0.03017 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Number of observations: total=166, day_ID=18
Random effect variance(s):
Group=day_ID
            Variance StdDev
(Intercept)   0.6564 0.8102

Negative binomial dispersion parameter: 1.7509 (std. err.: 1.2717)
Zero-inflation: 0.38013  (std. err.:  0.10838 )

Log-likelihood: -212.974
>

>From this ouput I conclude that wea1 has a significant effect on the number
of birds (nbird). But how do I know if the number of birds decreased or
increased?  If I tested this with a lmer, I would use:

*library(arm)
nsim <- 2000
bsim <- sim(mod, nsim)
apply(bsim@fixef,2,quantile,probs=c(0.025,0.5,0.975))*

and then create a new data.frame and plot the effects. However, the
function '*sim*' apparently doesn't work for glmmadmb:

>* bsim <- sim(mod7, nsim=nsim)*
Error in (function (classes, fdef, mtable)  :
  unable to find an inherited method for function ‘sim’ for signature
‘"glmmadmb"'

If I had a glmer with a binomial response variable, I would use *plogis() *to
test for the probability of a certain event to occur...

But how does it work for a glmmadmb? I'm sure I'm missing something
(obvious) here!?

Any help or suggestions would be very much appreciated!

Julia

*PS. buff_2000 below*


  area day_ID wea1 nbird  0.14 1 0 0  0.32 2 0 0  0.15 4 0 1  0.39 5 0 0
0.27 104 1 0  1.24 106 1 0  0.18 113 1 0  0.18 114 1 0  0.67 115 1 2  0.28 1
0 0  0.6 2 0 2  0.28 3 0 0  0.63 4 0 0  0.57 5 0 0  0.36 6 0 0  0.4 101 1 0
0.4 102 1 0  1.35 104 1 0  1.31 106 1 8  0.4 108 1 0  0.3 109 1 0  0.3 110 1
0  0.3 111 1 1  0.3 112 1 0  0.63 113 1 2  0.72 114 1 2  1.28 115 1 5  0.3 2
0 1  0.28 3 0 0  0.78 4 0 1  0.36 6 0 0  0.3 101 1 0  0.3 102 1 0  1.35 104
1 0  1.38 106 1 2  0.3 108 1 0  0.3 109 1 0  0.3 110 1 0  0.3 111 1 0  0.3
112 1 2  0.72 113 1 1  0.96 114 1 0  1 115 1 4  0.32 2 0 0  0.36 5 0 5  0.18
104 1 0  1.47 106 1 1  0.09 113 1 0  0.43 114 1 0  0.37 115 1 2  0.16 2 0 0
0.15 4 0 0  0.19 5 0 0  0.4 6 0 0  0.3 101 1 0  1.71 104 1 1  0.95 106 1 2
1 108 1 1  0.7 109 1 2  0.28 110 1 0  0.4 111 1 0  0.8 112 1 0  0.18 113 1 0
0.9 114 1 2  1.48 115 1 5  0.28 1 0 2  0.3 2 0 0  0.37 5 0 3  0.72 6 0 0
0.3 101 1 0  0.3 102 1 0  0.72 104 1 0  0.73 106 1 0  0.3 108 1 0  0.3 109 1
0  0.4 110 1 0  0.3 111 1 0  0.4 112 1 0  0.36 113 1 1  0.36 114 1 0  0.62
115 1 7  0.13 2 0 0  0.28 3 0 7  0.72 6 0 0  0.4 101 1 0  0.28 102 1 0  0.72
104 1 0  1.58 106 1 0  0.3 108 1 1  0.3 109 1 0  0.3 110 1 0  0.3 111 1 2
0.4 112 1 5  0.45 113 1 0  1.16 114 1 2  0.92 115 1 4  0.13 1 0 0  1.62 104
1 0  1.58 106 1 2  0.18 113 1 2  1.63 114 1 1  1.54 115 1 10  0.14 1 0 0
0.16 2 0 0  0.14 3 0 1  0.15 4 0 0  0.2 5 0 0  2.8 6 0 0  1.4 101 1 2  1.7
102 1 0  0.72 104 1 0  0.91 106 1 2  0.6 108 1 0  1.1 109 1 3  1.46 110 1 0
1.3 111 1 1  0.9 112 1 0  0.18 113 1 0  0.63 114 1 0  0.81 115 1 3  0.28 1 0
3  0.15 2 0 2  0.14 3 0 0  0.16 4 0 1  0.19 5 0 7  0.36 6 0 2  0.3 101 1 0
0.3 102 1 0  0.64 104 1 0  0.72 106 1 0  0.3 108 1 0  0.4 109 1 0  0.3 110 1
0  0.3 111 1 0  0.3 112 1 0  0.35 113 1 1  0.27 114 1 0  0.63 115 1 1  0.36
6 0 0  0.3 101 1 0  0.4 102 1 0  0.63 104 1 0  1.38 106 1 0  0.3 108 1 0
0.3 109 1 0  0.3 110 1 4  0.3 111 1 0  0.3 112 1 6  0.27 113 1 0  0.88 114 1
2  0.64 115 1 0  0.26 1 0 6  0.13 3 0 0  2.8 6 0 0  1.6 101 1 0  1.5 102 1 0
0.81 104 1 1  0.95 106 1 0  1.5 108 1 1  1.48 109 1 23  1.5 110 1 0  1.5 111
1 0  1.5 112 1 0  0.18 113 1 0  0.69 114 1 2  0.53 115 1 4

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