Thank you for your fast reply!

I'm willing to try the nu SIF method, but I have some questions.

1. To make sure, the equation of nu is:

SIF = (psiBA/(1-psiBA) / (psiBa/(1-psiBa))

2. If I want to use nu as SIF, can I calculate the CI simply by changing the "phi" in the code you provided above to "nu"? Should I change the parameterization from PsiBA to nu? The output of these parameterization methods seems similar.

3. The values and SEs of nu and phi are much different. Not sure if it's normal.

- Code: Select all
`> dmfit1$derived[[1]]$nu`

est se

1 99.999 99.999

2 99.999 99.999

3 99.999 99.999

4 99.999 99.999

5 99.999 99.999

6 99.999 99.999

7 99.999 99.999

> dmfit1$derived[[1]]$phi

est se

1 1.384 0.099

2 1.402 0.134

3 1.354 0.083

4 1.411 0.156

5 1.439 0.241

6 1.334 0.098

7 1.359 0.081

And this is the output of the code you provided. They both show co-occurrence patterns but with very different values.

- Code: Select all
`# nu SIF`

> cat(sif.mean, sqrt(sif.var), sif.mean + c(-1.96, 0, 1.96)*sqrt(sif.var), '\n')

95.21921 14.818 66.17593 95.21921 124.2625

# Richmond et al. 2010 SIF

> cat(sif.mean, sqrt(sif.var), sif.mean + c(-1.96, 0, 1.96)*sqrt(sif.var), '\n')

1.361635 0.03326755 1.296431 1.361635 1.426839

4. Do the estimated SIF and CIs above need to be back-transformed? Like

True SIF = EXP(SIF untransformed)/(1+EXP(SIF untransformed))

5. I read some papers and found a factor called Rho. It's the detection version of nu SIF. The equation is:

rho = (rBA/(1-rBA))/(rBa/(1-rBa))

Also, I read Jim's post and found the factor Lam = rAB/(rA*rB). I guess that's the conditional version of Rho.

The question is: where can I find these factors in the model output?