model.nbp.v: Modeling NBP Regression Model with Maximum Likelihood (ML) or...

In gu-mi/NBGOF: Goodness-of-Fit Tests and Model Diagnostics for Negative Binomial Regression of RNA Sequencing Data

Description

This function is designed to fit an NBP regression model. The output of this function will be passed to the main GOF function nb.gof.v.

Usage

model.nbp.v(y, x, lib.sizes=NULL, method="ML")

Arguments

y	an n-by-1 vector of non-negative integers. For a typical RNA-Seq experiment, this may represent the read counts for a single gene across n samples.
x	an n-by-p design matrix. If an intercept is desired in the model, you need to specify the first column of x as a vector of 1.
lib.sizes	library sizes of a RNA-Seq experiment. Default is 1 for all samples.
method	estimation method for NBP model fit. Either maximum likelihood (ML) or adjusted profile likelihood (APL). Default is ML.

Details

The glm.nbp.1.MLE function is used for NBP model fitting with MLE. The glm.nbp.1 function is used for NBP model fitting with APLE.

Value

A list of quantities to be used in the main nb.gof.v function.

Author(s)

Gu Mi <[e-mail]>, Yanming Di, Daniel Schafer

References

Di Y, Schafer DW, Cumbie JS, and Chang JH (2011): "The NBP Negative Binomial Model for Assessing Differential Gene Expression from RNA-Seq", Statistical Applications in Genetics and Molecular Biology, 10 (1).

See https://github.com/gu-mi/NBGOF/wiki/ for more details.

gu-mi/NBGOF documentation built on Oct. 25, 2020, 3:30 a.m.