Description Usage Arguments Details Value Author(s) Examples

View source: R/weightedCondLogLikDerDelta.R

Weighted conditional log-likelihood parameterized in terms of delta (`phi / (phi+1)`

) for a given gene, maximized to find the smoothed (moderated) estimate of the dispersion parameter

1 | ```
weightedCondLogLikDerDelta(y, delta, tag, prior.n=10, ntags=nrow(y[[1]]), der=0)
``` |

`y` |
list with elements comprising the matrices of count data (or pseudocounts) for the different groups |

`delta` |
delta ( |

`tag` |
gene at which the weighted conditional log-likelihood is evaluated |

`prior.n` |
smoothing paramter that indicates the weight to put on the common likelihood compared to the individual gene's likelihood; default |

`ntags` |
numeric scalar number of genes in the dataset to be analysed |

`der` |
derivative, either 0 (the function), 1 (first derivative) or 2 (second derivative) |

This function computes the weighted conditional log-likelihood for a given gene, parameterized in terms of delta. The value of delta that maximizes the weighted conditional log-likelihood is converted back to the `phi`

scale, and this value is the estimate of the smoothed (moderated) dispersion parameter for that particular gene. The delta scale for convenience (delta is bounded between 0 and 1).
Users should note that ‘tag’ and ‘gene’ are synonymous when interpreting the names of the arguments for this function.

numeric scalar of function/derivative evaluated for the given gene and delta

Mark Robinson, Davis McCarthy

1 2 3 4 5 | ```
counts<-matrix(rnbinom(20,size=1,mu=10),nrow=5)
d<-DGEList(counts=counts,group=rep(1:2,each=2),lib.size=rep(c(1000:1001),2))
y<-splitIntoGroups(d)
ll1<-weightedCondLogLikDerDelta(y,delta=0.5,tag=1,prior.n=10,der=0)
ll2<-weightedCondLogLikDerDelta(y,delta=0.5,tag=1,prior.n=10,der=1)
``` |

edgeR documentation built on Oct. 3, 2018, 6:01 p.m.

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