rfit {Rfit} | R Documentation |
Rank-based Estimates of Regression Coefficients
Description
Minimizes Jaeckel's dispersion function to obtain a rank-based solution for linear models.
Usage
rfit(formula, data = list(), ...)
## Default S3 method:
rfit(formula, data, subset, yhat0 = NULL,
scores = Rfit::wscores, symmetric = FALSE, TAU = "F0",
betahat0 = NULL, ...)
Arguments
formula |
an object of class formula |
data |
an optional data frame |
subset |
an optional argument specifying the subset of observations to be used |
yhat0 |
an n by 1 vector of initial fitted values, default is NULL |
scores |
an object of class 'scores' |
symmetric |
logical. If 'FALSE' uses median of residuals as estimate of intercept |
TAU |
version of estimation routine for scale parameter. F0 for Fortran, R for (slower) R, N for none |
betahat0 |
a p by 1 vector of initial parameter estimates, default is NULL |
... |
additional arguments to be passed to fitting routines |
Details
Rank-based estimation involves replacing the L2 norm of least squares estimation with a pseudo-norm which is a function of the residuals and the scored ranks of the residuals. That is, in rank-based estimation, the usual notion of Euclidean distance is replaced with another measure of distance which is referred to as Jaeckel's (1972) dispersion function. Jaeckel's dispersion function depends on a score function and a library of commonly used score functions is included; eg., linear (Wilcoxon) and normal (Gaussian) scores. If an inital fit is not supplied (i.e. yhat0 = NULL and betahat0 = NULL) then inital fit is based on a LS fit.
Esimation of scale parameter tau is provided which may be used for inference.
Value
coefficients |
estimated regression coefficents with intercept |
residuals |
the residuals, i.e. y-yhat |
fitted.values |
yhat = x betahat |
xc |
centered design matrix |
tauhat |
estimated value of the scale parameter tau |
taushat |
estimated value of the scale parameter tau_s |
betahat |
estimated regression coefficents |
call |
Call to the function |
Author(s)
John Kloke, Joesph McKean
References
Hettmansperger, T.P. and McKean J.W. (2011), Robust Nonparametric Statistical Methods, 2nd ed., New York: Chapman-Hall.
Jaeckel, L. A. (1972). Estimating regression coefficients by minimizing the dispersion of residuals. Annals of Mathematical Statistics, 43, 1449 - 1458.
Jureckova, J. (1971). Nonparametric estimate of regression coefficients. Annals of Mathematical Statistics, 42, 1328 - 1338.
See Also
summary.rfit
drop.test
rstudent.rfit
Examples
data(baseball)
data(wscores)
fit<-rfit(weight~height,data=baseball)
summary(fit)
### set the starting value
x1 <- runif(47); x2 <- runif(47); y <- 1 + 0.5*x1 + rnorm(47)
# based on a fit to a sub-model
rfit(y~x1+x2,yhat0=fitted.values(rfit(y~x1)))
### set value of delta used in estimation of tau ###
w <- factor(rep(1:3,each=3))
y <- rt(9,9)
rfit(y~w)$tauhat
rfit(y~w,delta=0.95)$tauhat # recommended when n/p < 5