View source: R/srcImpulseDE2_fitImpulse.R
fitConstModel | R Documentation |
[Model fitting function hierarchy: 4 out of 4] This quarterny fitting wrapper performs constant model fitting: This function executes numerical optimisaiton and error-handling thereof.
fitConstModel(vecCounts, scaDisp, vecSizeFactors, lsvecidxBatch, MAXIT = 1000, RELTOL = 10^(-8), trace = 0, REPORT = 10)
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. |
MAXIT |
(scalar) [Default 1000] Maximum number of BFGS iterations for model fitting with optim. |
RELTOL |
(scalar) [Default 10^(-8)] Maximum relative change in loglikelihood to reach convergence in numerical optimisation by BFGS in optim. |
trace |
(scalar) [Defaul 0] Reporting parameter of optim. |
REPORT |
(scalar) [Default 10] Reporting parameter of optim. |
(list) List of constant fit parameters and results.
scaMu (scalar) Maximum likelihood estimator of negative binomial mean parameter.
lsvecBatchFactors (list length number of confounders) List of vectors of scalar batch correction factors for each sample. These are also maximum likelihood estimators. NULL if no confounders given.
scaDispParam (scalar) Dispersion parameter estimate used in fitting (hyper-parameter).
scaLL (scalar) Loglikelihood of data under maximum likelihood estimator model.
scaConvergence (scalar) Convergence status of optim on constant model.
David Sebastian Fischer
Called by fitConstImpulseGene to fit constant model to samples of one condition and one gene. Calls constant model cost function evalLogLikMu within optim.
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