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Hello Jeff et al.,

Just a quick note to let you know I'm having trouble with the alpha statement for the covariate.predictions function in the MultScalOcc model.

Using the larksparrow example, I get the same CIs for the alphas in the code below:

Code: Select all

ceap.95 <- covariate.predictions(Species.results,data=data.frame(ceap=ceap.values),alpha=0.025)
ceap.90 <- covariate.predictions(Species.results,data=data.frame(ceap=ceap.values),alpha=0.05)


Thanks much.

Regards,

David

Thanks for this. Will look into it. --jeff

Hello All,

I'm curious if anyone else encountering this issue has found a solution? I have been attempting to predict survival in the nest survival model using an alpha that is not the default and have encountered the same issue.

Thanks much,
Michael

Sorry for dropping the ball on this. I haven't been good about following up once I retired. It isn't entirely clear in the documentation but the alpha parameter is only for the mata-wald confidence intervals. To use it you need to set argument mata=TRUE. I'll look into coding alpha for std confidence intervals. Will try to get to this by end of Sept. If you haven't heard from me by then, ping me. --jeff

From the documentation

The desired lower and upper error rate. Specifying alpha=0.025
corresponds to a 95% MATA-Wald confidence interval, an
alpha=0.05 to a 90% interval. 'alpha' must be between 0 and 0.5.
Default value is alpha=0.025.

Hi Jeff,

I was just curious if you'd had a chance to look into this? Appreciate all the work you've done already.

Thanks much,
Michael

Yes but haven't posted to cran. You can install from my GitHub site. I'm still testing some new models before I release