line {stats}  R Documentation 
Fit a line robustly as recommended in Exploratory Data Analysis.
Currently the initial medianmedian line is not iterated (as opposed to Tukey's “resistant line” in the references).
line(x, y, iter = 1)
x, y 
the arguments can be any way of specifying xy pairs. See

iter 
positive integer specifying the number of
“polishing” iterations. Note that this was hard coded to

Cases with missing values are omitted.
Contrary to the references where the data is split in three (almost)
equally sized groups with symmetric sizes depending on n and
n %% 3
and computes medians inside each group, the
line()
code splits into three groups using all observations
with x[.] <= q1
and x[.] >= q2
, where q1, q2
are
(a kind of) quantiles for probabilities p = 1/3 and p = 2/3
of the form (x[j1]+x[j2])/2
where j1 = floor(p*(n1))
and j2 = ceiling(p(n1))
, n = length(x)
.
Long vectors are not supported yet.
An object of class "tukeyline"
.
Methods are available for the generic functions coef
,
residuals
, fitted
, and print
.
Tukey, J. W. (1977). Exploratory Data Analysis, Reading Massachusetts: AddisonWesley.
Velleman, P. F. and Hoaglin, D. C. (1981). Applications, Basics and Computing of Exploratory Data Analysis, Duxbury Press. Chapter 5.
Emerson, J. D. and Hoaglin, D. C. (1983). Resistant Lines for y versus x. Chapter 5 of Understanding Robust and Exploratory Data Analysis, eds. David C. Hoaglin, Frederick Mosteller and John W. Tukey. Wiley.
Iain M. Johnstone and Paul F. Velleman (1985). The Resistant Line and Related Regression Methods. Journal of the American Statistical Association, 80, 1041–1054. doi: 10.2307/2288572.
lm
.
There are alternatives for robust linear regression more robust and
more (statistically) efficient,
see rlm()
from MASS, or
lmrob()
from robustbase.
require(graphics) plot(cars) (z < line(cars)) abline(coef(z)) ## TukeyAnscombe Plot : plot(residuals(z) ~ fitted(z), main = deparse(z$call)) ## Andrew Siegel's pathological 9point data, yvalues multiplied by 3: d.AS < data.frame(x = c(4:3, 12), y = 3*c(rep(0,6), 5, 5, 1)) cAS < with(d.AS, t(sapply(1:10, function(it) line(x,y, iter=it)$coefficients))) dimnames(cAS) < list(paste("it =", format(1:10)), c("intercept", "slope")) cAS ## iterations started to oscillate, repeating iteration 7,8 indefinitely