gemini_inference {gemini} | R Documentation

## gemini_inference

### Description

Estimate the posterior using a variational inference technique. Inference is performed through an iterative process until convergence.

### Usage

    gemini_inference(Model, n_iterations = 20, mean_x = 1, sd_x = 1,
      mean_xx = 1, sd_xx = 1, mean_y = 0, sd_y = 10, mean_s = 0,
      sd_s = 10, threshold = 0.001, cores = 1, force_results = FALSE,
      verbose = FALSE, save_iterations = FALSE)

### Arguments

`Model` | an object of class gemini.model
`n_iterations` | a numeric indicating the maximum number of iterations (default=20).
`mean_x` | a numeric indicating prior mean of x (default=1).
`sd_x` | a numeric indicating prior sd of x (default=1).
`mean_xx` | a numeric indicating prior mean of xx (default=1).
`sd_xx` | a numeric indicating prior sd of xx (default=1)
`mean_y` | a numeric indicating prior mean of y (default=0)
`sd_y` | a numeric indicating prior sd of y (default=10).
`mean_s` | a numeric indicating prior mean of s(default=0)
`sd_s` | a numeric indicating prior sd of s (default=10)
`threshold` | a numeric indicating the threshold of change in MAE at which to stop the iterative process (default=0.001).
`cores` | a numeric indicating the number of cores to use. See details in gemini_parallel. (default=1)
`force_results` | a logical indicating if the CAVI algorithm should be halted if non-convergence is detected. (default=FALSE)
`verbose` | default FALSE
`save_iterations` | for especially large libraries that require long computations, saves the latest iteration of each update. default FALSE

### Details

GEMINI uses the following parameters, which are described in Zamanighomi et al. and translated here for clarity:

- y: individual gene effect

- s: combination effect

- x: screen variation corresponding to individual guides

- xx: screen variation corresponding to paired guides

Default parameters may need to be changed if convergence is not achieved. See README for more details.

### Value

a gemini.model object with estimated posteriors

### Examples

    data("Model", package = "gemini")
    Model %<>% gemini_inference(verbose = FALSE, n_iterations = 1) # iterations set to 1 for testing

[Package gemini version 1.2.0 Index]