Description Usage Arguments Value See Also Examples

Wrapper to actually run the Expectation-maximization algorithm and estimate $f_count$ fits. Maximum-likelihood estimates are approximated using the EM algorithm where we treat mixture membership $delta_ij = 1$ if $y_ij$ is generated from the zero point mass as latent indicator variables. The density is defined as $f_zig(y_ij = pi_j(S_j)*f_0(y_ij) +(1-pi_j (S_j)) * f_count(y_ij; mu_i, sigma_i^2)$. The log-likelihood in this extended model is: $(1-delta_ij) log f_count(y;mu_i,sigma_i^2 )+delta_ij log pi_j(s_j)+(1-delta_ij) log (1-pi_j (s_j))$. The responsibilities are defined as $z_ij = pr(delta_ij=1 | data)$.

1 2 3 4 5 6 7 8 9 |

`obj` |
A MRexperiment object with count data. |

`mod` |
The model for the count distribution. |

`zeroMod` |
The zero model, the model to account for the change in the number of OTUs observed as a linear effect of the depth of coverage. |

`useCSSoffset` |
Boolean, whether to include the default scaling parameters in the model or not. |

`control` |
The settings for fitZig. |

`useMixedModel` |
Estimate the correlation between duplicate features or replicates using duplicateCorrelation. |

`...` |
Additional parameters for duplicateCorrelation. |

A list of objects including:

call - the call made to fitZig

fit - 'MLArrayLM' Limma object of the weighted fit

countResiduals - standardized residuals of the fit

z - matrix of the posterior probabilities

eb - output of eBayes, moderated t-statistics, moderated F-statistics, etc

taxa - vector of the taxa names

counts - the original count matrix input

zeroMod - the zero model matrix

zeroCoef - the zero model fitted results

stillActive - convergence

stillActiveNLL - nll at convergence

dupcor - correlation of duplicates

1 2 3 4 5 6 7 8 9 10 11 12 | ```
# This is a simple demonstration
data(lungData)
k = grep("Extraction.Control",pData(lungData)$SampleType)
lungTrim = lungData[,-k]
k = which(rowSums(MRcounts(lungTrim)>0)<30)
lungTrim = cumNorm(lungTrim)
lungTrim = lungTrim[-k,]
smokingStatus = pData(lungTrim)$SmokingStatus
mod = model.matrix(~smokingStatus)
# The maxit is not meant to be 1 - this is for demonstration/speed
settings = zigControl(maxit=1,verbose=FALSE)
fit = fitZig(obj = lungTrim,mod=mod,control=settings)
``` |

metagenomeSeq documentation built on Nov. 8, 2020, 5:34 p.m.

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