Hi,
I am using a two part hurdle model to account for zero inflation and
overdispersion in my count data. I would like to account for a segmented or
breakpoint relationship in the binomial logistic hurdle model and pass
these results onto the count model (negative binomial).
Using the segemented package I have determined that my data supports one
breakpoint at 3.85. The slope to this point is significant and will affect
the presence of zeros in a linear fashion. The slope > 3.85 is
non-significant and estimated to not help predict the presence of zeros in
the data (threshold effect). Here are the results from this model
Estimated Break-Point(s):
Est. St.Err
3.853 1.372
t value for the gap-variable(s) V: 0
Meaningful coefficients of the linear terms:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.2750 0.3556 -0.774 0.4392
approach_km -0.4520 0.2184 -2.069 0.0385 *
sea2 0.3627 0.2280 1.591 0.1117
U1.approach_km 0.4543 0.2188 2.076 NA
U1.approach_km is the estimate for the second slope. The actual estimated
slope for the section section is the difference between this value and the
approach_km value (0.0023).
I think that I have found a way to "maually" code this into the hurdle
model as follows
hurdle.fit <- hurdle(tot_f ~ x1 + x2 + x3 | approach_km +
I(pmax(approach_km-3.849,0)) + sea )
When I look at the estimated coefficients from the "manual" code it gives
the same values. However, the std.errors are estimated lower.
Zero hurdle model coefficients (binomial with logit link):
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.27441 0.29347 -0.935 0.350
approach_km -0.45261 0.09993 -4.529 5.92e-06 ***
I(pmax(approach_km - 3.849, 0)) 0.45486 0.10723 4.242 2.22e-05 ***
sea2 0.36271 0.22803 1.591 0.112
Question # 1: Does the hurdle equation use the standard errors from the
zero model when building the count predictions? If no then I guess I would
not have to worry about this and can just report the original std.errors
and associated p values from the segemented object in the pub.
Question # 2: If the count model uses the std.errors, how can I reformulate
this equation to generate the original std.errors.
Thanks, Tim
[[alternative HTML version deleted]]
______________________________________________
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
