iterCIR: Centered-isotonic-regression (CIR): iterative version for...

In cir: Centered Isotonic Regression and Dose-Response Utilities

Centered-isotonic-regression (CIR): iterative version for better bias correction

Description

EXPERIMENTAL: Nonparametric forward point estimation of a monotone response (y) as a function of dose (x), using an iterative version of the centered-isotonic-regression (CIR) algorithm. The code works, but delivers marginal improvement at greater computational cost (an issue if you simulate a large ensemble), and somewhat convoluted interpretation. Use at your own risk. For explanation, see Oron and Flournoy (2017), Section 3.2.

Usage

iterCIR(y, outx = NULL, tol = 0.001, maxit = 10, full = FALSE, ...)

Arguments

y	See cirPAVA
outx	vector of x values for which predictions will be made. If NULL (default), this will be set to the set of unique values in the x argument (or equivalently in y$x).
tol	The iteration's convergence tolerance level (default 1e-3)
maxit	integer, maximum number of iterations (default 10)
full	logical, is a more complete output desired? if FALSE (default), only a vector of point estimates for y at the provided dose levels is returned
...	Other arguments passed on to cirPAVA

Value

under default, returns a vector of y estimates at unique x values. With full=TRUE, returns a list of 3 doseResponse objects named output,input,shrinkage for the output data at dose levels, the input data, and the function as fit at algorithm-generated points, respectively.

See Also

cirPAVA,quickIsotone

cir documentation built on April 27, 2023, 9:05 a.m.