meanVarParaFit fits a mean-variance curve by applying a robust,
gamma-family glm regression, taking advantage of the
form: \(var = c1 + c2 / (2 ^ mean)\).
meanVarParaFit(
x,
y,
weight,
range.residual = c(1e-04, 15),
max.iter = 50,
init.coef = NULL,
verbose = TRUE
)A prediction function which accepts a vector of means and returns
the predicted variances, with an attribute named "coefficients"
attached.
Two numeric vectors of (sample) means and sample variances, respectively.
Two numeric vectors of (sample) means and sample variances, respectively.
An optional vector of weights to be used in the fitting
procedure. It's typically used when sample variances in y are
associated with different numbers of degrees of freedom.
A length-two vector specifying the range of residuals of non-outliers.
Maximum number of iteration times allowed during the fitting procedure.
An optional length-two vector specifying the initial values of the coefficients.
Whether to print processing messages about iteratively fitting the mean-variance curve?
meanVarParaFit iteratively detects outliers and fits a generalized
linear model on non-outliers. The procedure converges as soon as the set of
outlier points fixes.
See "References" for the theoretical foundation of the parametric form.
Robinson, M.D. and G.K. Smyth, Small-sample estimation of negative binomial dispersion, with applications to SAGE data. Biostatistics, 2008. 9(2): p. 321-32.
Love, M.I., W. Huber, and S. Anders, Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol, 2014. 15(12): p. 550.
Tu, S., et al., MAnorm2 for quantitatively comparing groups of ChIP-seq samples. Genome Res, 2021. 31(1): p. 131-145.
meanVarLocalFit for using local regression to fit
a mean-variance curve; fitMeanVarCurve for an interface
to modeling the mean-variance dependence on bioCond
objects; plotMeanVarCurve for plotting a mean-variance
curve.