Description

negLLzeroSquash computes the negative log-likelihood based on the unconditional marginal distribution of N (DuMouchel et al. 2001). This function is minimized to estimate the hyperparameters of the prior distribution. Use this function if including zero counts and using data squashing. Generally this function is not recommended unless convergence issues occur without zero counts (negLLsquash is typically more efficient).

Usage

Arguments

Details

The marginal distribution of the counts, N, is a mixture of two negative binomial distributions. The hyperparameters for the prior distribution (mixture of gammas) are estimated by optimizing the likelihood equation from this marginal distribution.

The hyperparameters are:

• α_1, β_1: Parameters of the first component of the marginal distribution of the counts (also the prior distribution)

• α_2, β_2: Parameters of the second component

• P: Mixture fraction

This function will not need to be called directly if using exploreHypers or autoHyper.

Value

A scalar negative log-likelihood value.

Warnings

Make sure ni actually contains zeroes before using this function. You should have used the zeroes = TRUE option when calling the processRaw function.

Make sure the data were actually squashed (see squashData) before using this function.

References

DuMouchel W, Pregibon D (2001). "Empirical Bayes Screening for Multi-item Associations." In Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '01, pp. 67-76. ACM, New York, NY, USA. ISBN 1-58113-391-X.

See Also

nlm, nlminb, and optim for optimization and squashData for data squashing

Other negative log-likelihood functions: negLLsquash(), negLLzero(), negLL()