bigRfit | R Documentation |
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.
bigRfit(x, y, B = 1001, scores = Rfit::wscores, max.iter = 100, eps = (.Machine$double.eps)^0.625)
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 |
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) |
John Kloke johndkloke@gmail.com
Hettmansperger, T.P. and McKean J.W. (2011), Robust Nonparametric Statistical Methods, 2nd ed., New York: Chapman-Hall.
Rfit
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)
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