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meanVarLocalFit fits a mean-variance curve by applying a robust,
gamma-family local regression.
meanVarLocalFit(
x,
y,
weight,
range.residual = c(1e-04, 15),
max.iter = 50,
args.lp = list(),
args.locfit = list(),
verbose = TRUE
)A prediction function which accepts a vector of means and returns the predicted variances.
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.
A named list of extra arguments to lp.
A named list of extra arguments to
locfit.
Whether to print processing messages about iteratively fitting the mean-variance curve?
meanVarLocalFit iteratively detects outliers and applies the local
regression procedure to non-outliers. The procedure converges as soon as the
set of outlier points fixes.
meanVarParaFit for parametrically fitting a
mean-variance curve; fitMeanVarCurve for an interface to
modeling the mean-variance dependence on bioCond objects;
plotMeanVarCurve for plotting a mean-variance curve.