evalLogLikMu: Cost function for constant model

In YosefLab/ImpulseDE2: Differential expression analysis of longitudinal count data sets

Cost function for constant model

Description

Log likelihood cost function for numerical optimisation of constant model. Implements log linker function for the constant mean parameter and the batch correction factors. Implements lower sensitivity bound of likelihood with respect to constant mean parameter. Implements upper and lower sensitivity bound of likelihood with respect to batch correction factors.

Usage

evalLogLikMu(vecTheta, vecCounts, scaDisp, vecSizeFactors, lsvecidxBatch,
  vecboolObserved)

Arguments

vecTheta	(numeric vector number of parameters to be estimated) Constant model parameter and batch correction factor estimates.
vecCounts	(numeric vector number of samples) Read count data.
scaDisp	(scalar) Gene-wise negative binomial dispersion hyper-parameter.
vecSizeFactors	(numeric vector number of samples) Model scaling factors for each sample which take sequencing depth into account (size factors).
lsvecidxBatch	(list length number of confounding variables) List of index vectors. One vector per confounding variable. Each vector has one entry per sample with the index batch within the given confounding variable of the given sample. Batches are enumerated from 1 to number of batches.
vecboolObserved	(bool vector number of samples) Whether sample is observed (finite and not NA).

Value

scaLogLik (scalar) Value of cost function (loglikelihood) for given gene.

Author(s)

David Sebastian Fischer

See Also

Compiled version: evalLogLikMu_comp

YosefLab/ImpulseDE2 documentation built on Sept. 17, 2022, 2:45 a.m.