HZINB_ind_two_gamma: HGZIPS - HZINB with two gamma components (assuming...
In sidiwang/hgzips: hgzips

Description Usage Arguments Value

View source: R/HZINB_ind_two_gamma.R

This HZINB_ind_two_gamma function finds hyperparameter estimates by implementing the Expectation-Maximization (EM) algorithm and hierarchical zero-inflated negative binomial model with two gamma components.

grid_HZINB_two_gamma(a_j, b_j, omega_j, K1, L1, K2, L2, M, H)

HZINB_ind_two_gamma(
  grid_a1,
  grid_a2,
  grid_b1,
  grid_b2,
  grid_pi,
  grid_omega,
  init_pi_k1,
  init_pi_l1,
  init_pi_k2,
  init_pi_l2,
  init_pi_m,
  init_pi_h,
  dataset,
  iteration,
  Loglik = FALSE,
  zeroes = FALSE,
  N_star = 1
)

`grid_a1`	alpha1 value grid
`grid_a2`	alpha2 value grid
`grid_b1`	beta1 value grid
`grid_b2`	beta2 value grid
`grid_pi`	pi value grid
`grid_omega`	omega value grid
`init_pi_k1`	initial probability of each alpha1 value for implementing the EM algorithm
`init_pi_l1`	initial probability of each beta1 value for implementing the EM algorithm
`init_pi_k2`	initial probability of each alpha2 value for implementing the EM algorithm
`init_pi_l2`	initial probability of each beta2 value for implementing the EM algorithm
`init_pi_m`	initial probability of each pi value for implementing the EM algorithm
`init_pi_h`	initial probability of each omega value for implementing the EM algprithm
`dataset`	a list of squashed datasets that include N_ij, E_ij and weights for each drug (j). This dataset list can be generated by the rawProcessing function in this package.
`iteration`	number of EM algorithm iterations to run
`Loglik`	whether to return the loglikelihood of each iteration or not (TRUE or FALSE)
`zeroes`	A logical scalar specifying if zero counts should be included.
`N_star`	the minimum Nij count size to be used for hyperparameter estimation. If zeroes are included in Nij vector, please set N_star = NULL

grid_HZINB build a suitable grid of a_j, b_j, and omega_j for implementing HZINB grid_HZINB

HZINB_ind_two_gamma a list of estimated probability of each alpha1, beta1, alpha2, beta2, pi, omega combination and their corresponding loglikelihood (optional)