How is Std Error for Beta Binomial Distribution Analysis calculated? - differences between R and JMP

[#1]

Hi All!

We are calculating beta binomial distribution parameters in R and wanted to compare the results and the std error.

Typically, the values for location and dispersion are up to 1% higher in JMP and there is also some discrepancy in the std error.

We would like to match here our R-code. 

May anybody provide a hint how the standard error for mu and rho are calculated? Fisher Matrix?

Thanks

[#2]

Have you seen the JMP Help about this topic? Is it possible that the R function or model uses a different parameterization?

[#3]

I found this in your documentation:

Var(x) = np(1-p)[1+(n-1)δ]

Is this the Std Error shown in the table?

[#4]

No, that quantity is the variance of the random variable when assuming a beta binomial distribution.

[#5 · Accepted solution]

JMP uses the inverse of the information matrix to estimate the covariance matrix (and standard errors). https://en.wikipedia.org/wiki/Fisher_information

[#6]

Thank you very much. This is what I thought.