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).
negLLzeroSquash(theta, ni, ei, wi)A scalar negative log-likelihood value.
A numeric vector of hyperparameters ordered as: \(\alpha_1, \beta_1, \alpha_2, \beta_2, P\).
A whole number vector of squashed actual counts from
squashData.
A numeric vector of squashed expected counts from
squashData.
A whole number vector of bin weights from squashData.
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.
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:
\(\alpha_1, \beta_1\): Parameters of the first component of the marginal distribution of the counts (also the prior distribution)
\(\alpha_2, \beta_2\): Parameters of the second component
\(P\): Mixture fraction
This function will not need to be called directly if using
exploreHypers or autoHyper.
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.