bigRreg: Rank-based regression for big data.

In kloke/bigRfit: Big data version of Rfit

Rank-based regression for big data.

Usage

bigRreg(formula, data, ...)

Arguments

formula	formula
data	data frame
intercept	indicator to request estimate of alpha (FALSE by default)
yhat0	optional initial estimate of responses
ehat0	optional initial estimate of residuals
B	number of bins to use (default of 1000)
scores	an object of class 'scores' (wscores by default)
max.iter	maximum number of iterations
eps	specify tol
TAU	version of estimation routine for scale parameter. DT for approximate binned estimate (using data.table), F0 for Fortran using full set of residuals, N for none \item ... additional arguments passed to fitting routines

Kloke, McKean (2023) "Nonparametric Statistical Methods using R" John Kloke <[e-mail]>

## n <- 8000 x1 <- rt(n,9) ; x2 <- rt(n,9) y <- x1 + rt(n,9) d <- data.frame(x1,x2,y) bigRreg(y~1+x1+x2,data=d) # fit model with intercept bigRreg(y~x1+x2,data=d) # same bigRreg(y~x1+x2-1,data=d) # same

## v0.8.1 December 2022 ## ## The function is currently defined as function (formula, data, ...) call <- match.call() res <- bigRfit_xc(formula, data, intercept = TRUE, ...) res$call <- call res$betahat0 <- with(res, alphahat - drop(crossprod(xbar, betahat))) class(res) <- append(class(res), "bigRreg", 0) res

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