Description Usage Arguments Value Examples

This function directly maximize the log likelihood function through optimization.
With this function, three models can be fitted: (1) negative binomial mixture with equal dispersion (E model);
(2) negative binomial mixture with unequal dispersion (V model); (3) 0-inflated negative binomial model.
The 0-inflated negative binomial has the following density function:
*P(Y=y)=π D(y) + (1-π)NB(μ, φ)* where D is the point mass at 0 while *NB(μ, φ)* is the density
of negative binomial distribution with mean *μ* and dispersion *φ*. The variance is *μ+φ μ^2*.
The rule to fit 0-inflated model is that the observed percentage of count exceeds the user specified threshold. This
rule overrides the model argument when observed percentae of zero count exceeds the threshold.

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`y` |
A vector representing the RNAseq raw count. |

`d` |
A vector of the same length as y representing the normalization constant to be applied to the data. For the LN model, the original data would be devided by this vector. |

`inits` |
Initial value to fit the mixture model. A vector with elements mu1, mu2, phi1, phi2 and pi1. For 0-inflated model, only mu2, phi2, pi1 are used while the other elements can be arbitrary. |

`model` |
Character specifying E or V model. E model fits the mixture model with equal variance while V model doesn't put any constraint. |

`zeroPercentThr` |
A scalar specifying the minimum percent of zero counts needed when fitting a zero-inflated Negative Binomial model. This parameter is used to deal with zero-inflation in RNAseq count data. When the percent of zero exceeds this threshold, rather than fitting a 2-component negative binomial mixture, a mixture of point mass at 0 and negative binomial is fitted. |

A vector consisting parameter estimates of mu1, mu2, sigma1, sigma2, pi1, logLik and BIC. For 0-inflated model, mu1=sigma1=0.

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