Description Usage Arguments Author(s) References
Computes the rank-based fit of a nonlinear model, either the Wilcoxon (“WIL”) or HBR fit. The Wilcoxon fit is discussed in Chapter 3 of Hettmansperger and McKean (2011) and the HBR fit is developed in Abebe and McKean (2014). See Section 7.7 of Kloke and McKean (2014) for a discussion of the Rfit version.
1 |
x |
matrix of predictors |
y |
response vector |
theta0 |
initial estimate of nonlinear parameters |
fmodel |
R function for the model |
jmodel |
R function for the Jacobian |
numstp |
maximum number of iterative steps (default is 50) |
eps |
precision tolerance (default is 0.001) |
wts.type |
either "WIL" (default) for the Wilcoxon fit or "HBR" for the HBR fit |
intest |
either "HL" (default) for Hodges-Lehmann estimator of the intercept or "MED" for the median estimator |
intercept |
TRUE if an intercept is in the model else FALSE |
Joe McKean mckean@wmich.edu and John Kloke kloke@biostat.wisc.edu
Abebe, A. and McKean, J.W. (2014), Weighted Wilcoxon estimators in nonlinear regression, Australian and New Zealand Journal of Statistics, 55, 401-420.
Hettmansperger, T.P. and McKean J.W. (2011), Robust Nonparametric Statistical Methods, 2nd ed., New York: Chapman-Hall.
Kloke, J. and McKean, J.W. (2014), Nonparametric statistical methods using R, Boca Raton, FL: Chapman-Hall.
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