evalLogLikSigmoid: Cost function for sigmoidal model
In ImpulseDE2: Differential expression analysis of longitudinal count data sets

Description Usage Arguments Value Author(s) See Also

View source: R/srcImpulseDE2_CostFunctionsFit.R

Log likelihood cost function for numerical optimisation of sigmoidal model. Implements log linker function for the amplitude parameters and the batch correction factors. Implements upper and lower sensitivity bound of likelihood with respect to batch correction factors and lower bound for amplitude paramters.

1 2	evalLogLikSigmoid(vecTheta, vecCounts, scaDisp, vecSizeFactors, vecTimepointsUnique, vecidxTimepoint, lsvecidxBatch, vecboolObserved)

`vecTheta`	(numeric vector number of parameters to be estimated) Sigmoid 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).
`vecTimepointsUnique`	(numeric vector length number of unique time points) Unique time points of set of time points of given samples.
`vecidxTimepoint`	(index vector length number of samples) Index of of time point assigned to each sample in vector vecTimepointsUnique.
`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).