I am trying to run a zero-inflated negative binomial for my data on lifetime inclusive fitness of birds. This is the distribution of fitness (dependent variable):
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.0000 0.0000 0.0000 0.3321 0.2072 3.8041
The independent variables are "prop.breeding": number of times a bird attempts to breed in its lifetime (it can nest multiple times a year), and "birthstatus": natal (N) or immigrant (I)
This is the model summary:
Call:
pscl::zeroinfl(formula = as.integer(inclusive.fitness_positive) ~ prop.breeding * birthstatus,
data = inclusive.fitness.all, dist = "negbin", link = "logit", maxit = 5e+06)
Pearson residuals:
Min 1Q Median 3Q Max
-0.9914 -0.2128 -0.2128 -0.1003 4.4865
Count model coefficients (negbin with log link):
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.3126 0.6628 1.980 0.0477 *
prop.breeding -0.3385 0.2552 -1.327 0.1846
birthstatusN -4.4074 0.8316 -5.300 1.16e-07 ***
prop.breeding:birthstatusN 2.4936 0.4214 5.918 3.27e-09 ***
Log(theta) 10.4413 NA NA NA
Zero-inflation model coefficients (binomial with logit link):
Estimate Std. Error z value Pr(>|z|)
(Intercept) 4.3586 1.2901 3.378 0.000729 ***
prop.breeding -1.6734 0.6668 -2.509 0.012092 *
birthstatusN -18.5287 13.4420 -1.378 0.168074
prop.breeding:birthstatusN 9.4203 6.8572 1.374 0.169510
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Theta = 34244.0508
Number of iterations in BFGS optimization: 36
Log-likelihood: -56.06 on 9 Df
I can't figure out why the standard error of log(theta) is NA.
Hello!
I am trying to run a zero-inflated negative binomial for my data on lifetime inclusive fitness of birds. This is the distribution of fitness (dependent variable):
The independent variables are "prop.breeding": number of times a bird attempts to breed in its lifetime (it can nest multiple times a year), and "birthstatus": natal (N) or immigrant (I)
This is the model summary:
I can't figure out why the standard error of log(theta) is NA.
Thanks for you help!