bigRfit: Rank-based estimation for linear models with big data.

bigRfitR Documentation

Rank-based estimation for linear models with big data.

Description

An implementation of rank-based estimation when working with big data. Uses step scores to increase the speed. Requires at least 2003 records in the dataset.

Usage

bigRfit(x, y, B = 1001, scores = Rfit::wscores, max.iter = 100, eps = (.Machine$double.eps)^0.625)

Arguments

x

n by p design matrix

y

n by 1 response vector

B

number of breaks (number of 'buckets' + 1)

scores

an object of class scores

max.iter

maximum number of iterations

eps

stopping criteria

Value

coefficients

estimated regression coefficents with intercept

residuals

the residuals, i.e. y-yhat

:

fitted.values

yhat = x betahat

scores

score function used in estimation

x

design matrix w/ intercept added (ie cbind(1,x))

y

original response vector

tauhat

estimated value of the scale parameter tau

taushat

estimated value of the scale parameter tau_s

symmetric

currently set to TRUE (needed for helper functions down the line)

iter

number of iterations

D1

final model dispersion

D0

null model dispersion

converage

convergance status (logical)

qrx1

result of call to qr of cbind(1,x)

Author(s)

John Kloke johndkloke@gmail.com

References

Hettmansperger, T.P. and McKean J.W. (2011), Robust Nonparametric Statistical Methods, 2nd ed., New York: Chapman-Hall.

See Also

Rfit

Examples

n <- 10^4; p <- 10
x <- matrix(rnorm(n*p),ncol=p)
y <- rnorm(n)
#system.time(fit0 <- rfit(y~x))
#summary(fit0)
#system.time(fit1 <- bigRfit(x,y))
#summary(fit1)

kloke/bigRfit documentation built on April 20, 2023, 4:33 p.m.