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Greetings,<br>
I am interested in RE variance estimates from a negative binomial
mixed model. My model has a random intercept, no fixed effects, 4
random effects: time, collector, location, facility. RE location is
nested in facility. Also RE location is crossed with time and
collector. After studying several examples I came up with this
syntax, is it correct:<br>
<br>
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<pre tabindex="0" class="GD40030CLR" style="font-family: 'Lucida Console'; font-size: 10pt !important; outline-style: none; outline-width: initial; outline-color: initial; border-top-style: none; border-right-style: none; border-bottom-style: none; border-left-style: none; border-width: initial; border-color: initial; white-space: pre-wrap !important; margin-top: 0px; margin-right: 0px; margin-bottom: 0px; margin-left: 0px; line-height: 1.3; ">glmmadmb(formula = Counts ~ 1 + (1 | time) + (1 | collector) + (1 | facility) + (1 | facility:location), data = Data, family = "nbinom", link = "log")</pre>
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<pre tabindex="0" class="GD40030CLR" style="font-family: 'Lucida Console'; font-size: 10pt !important; outline-style: none; outline-width: initial; outline-color: initial; border-top-style: none; border-right-style: none; border-bottom-style: none; border-left-style: none; border-width: initial; border-color: initial; white-space: pre-wrap !important; margin-top: 0px; margin-right: 0px; margin-bottom: 0px; margin-left: 0px; line-height: 1.3; ">
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.952 0.356 5.49 4.1e-08 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Number of observations: total=180, =3, =2, =4, =44
Random effect variance(s):
$time
(Intercept)
(Intercept) 2.9696e-09
$collector
(Intercept)
(Intercept) 0.00047214
$facility
(Intercept)
(Intercept) 0.26907
$`facility:location`
(Intercept)
(Intercept) 1.352
Negative binomial dispersion parameter: 1.379 (std. err.: 0.19336)
Log-likelihood: -570.014 </pre>
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<br>
If we think of r and p as the negative binomial parameters, how can
I estimate "r" for such a complex dataset?<br>
<br>
I would also like to estimate P, would you agree that it can be
estimated as:<br>
<br>
P =exponentiation of (<big><big><small>intercept+RE_facility+RE_location+RE_collector+RE_time)</small></big></big><br>
<br>
Sharif<br>
<br>
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