Description Usage Arguments Value

Run expectation maximization (EM) to estimate the parameters of the
multinomial mixture model. This function takes an SCE
object as input, and returns an SCE object with the EM output.
The number of clusters and their initialized multinomial means
are taken from the initial clustering assignments calculated with
`initialize_clusters`

. The EM algorithm is run
by repeatedly updating the membership probabilities and then
estimating the MLE parameters of the multinomial mixture model.
The algorithm converges when the percent change in
the log likihood is less than `eps`

. If the
algorithm doesn't converge by `max_iter`

, it breaks off.
The posterior probability is the calculated by taking the
sum of the likelihood fractions across the cell types.

1 2 |

`x` |
An SCE object. |

`eps` |
Numeric threshold. The EM algorithm converges when the
percent change in log likihood is less than |

`max_iter` |
The maximum number of iterations allowed to run. |

`psc` |
Pseudocount to add to multinomial parameters to avoid collapsing likelihood to 0. |

`verbose` |
Logical indicating verbosity. |

An SCE object.

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.