Dear All,
I am running a negative binomial model in R using the package pscl in oder to
estimate bed sediment movements versus river discharge. Currently we have
deployed 4 different plates to test if a combination of more than one plate
would better describe the sediment movements when the river discharge changes
over time.
My data are positively skewed and zero-inflated. I did run both zero-inflated
Poisson and zero-inflated negative binomial regression and compared them using
the VUONG test which showed that the negative binomial works better than a
simple zero-inflated Poisson.
My models look like:
1) plate1 ~ river discharge
2) (plate 1 + plate 2) ~ river discharge
3) (plate 1 + plate 2 +plate 3) ~ river discharge
4) (plate 1 + plate 2 + plate 3 + plate 4) ~ river discharge
My main problem as I am new to these type of models is that I get a different
sign for the coefficent of discharge in the output of the zero-inflated
negative binomial model (please see below). What does this mean? Also how could
I compare the different models (1-4) i.e. what tells me which is performing
best? Thank you very much in advance for any comments and suggestions!!
Kind Regards,
Valentina
Call:
zeroinfl(formula = plate1 ~ discharge, data = datafit_plates, dist = "negbin",
EM = TRUE)
Pearson residuals:
Min 1Q Median 3Q Max
-0.6770 -0.3564 -0.2101 -0.0814 12.3421
Count model coefficients (negbin with log link):
Estimate Std. Error z value Pr(>|z|)
(Intercept) 2.557066 0.036593 69.88 <2e-16 ***
discharge 0.064698 0.001983 32.63 <2e-16 ***
Log(theta) -0.775736 0.012451 -62.30 <2e-16 ***
Zero-inflation model coefficients (binomial with logit link):
Estimate Std. Error z value Pr(>|z|)
(Intercept) 13.01011 0.22602 57.56 <2e-16 ***
discharge -1.64293 0.03092 -53.14 <2e-16 ***
Theta = 0.4604
Number of iterations in BFGS optimization: 1
Log-likelihood: -6.933e+04 on 5 Df
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