Resistant Regression
Fit a regression to the good points in the dataset, thereby
achieving a regression estimator with a high breakdown point.
lmsreg and ltsreg are compatibility wrappers.
lqs(x, ...)
## S3 method for class 'formula'
lqs(formula, data, ...,
method = c("lts", "lqs", "lms", "S", "model.frame"),
subset, na.action, model = TRUE,
x.ret = FALSE, y.ret = FALSE, contrasts = NULL)
## Default S3 method:
lqs(x, y, intercept = TRUE, method = c("lts", "lqs", "lms", "S"),
quantile, control = lqs.control(...), k0 = 1.548, seed, ...)
lmsreg(...)
ltsreg(...)formula |
a formula of the form |
data |
an optional data frame, list or environemnt from which
variables specified in |
subset |
an index vector specifying the cases to be used in fitting. (NOTE: If given, this argument must be named exactly.) |
na.action |
function to specify the action to be taken if
|
model, x.ret, y.ret |
logical. If |
contrasts |
an optional list. See the |
x |
a matrix or data frame containing the explanatory variables. |
y |
the response: a vector of length the number of rows of |
intercept |
should the model include an intercept? |
method |
the method to be used. |
quantile |
the quantile to be used: see |
control |
additional control items: see |
k0 |
the cutoff / tuning constant used for chi()
and psi() functions when |
seed |
the seed to be used for random sampling: see |
... |
arguments to be passed to |
Suppose there are n data points and p regressors,
including any intercept.
The first three methods minimize some function of the sorted squared
residuals. For methods "lqs" and "lms" is the
quantile squared residual, and for "lts" it is the sum
of the quantile smallest squared residuals. "lqs" and
"lms" differ in the defaults for quantile, which are
floor((n+p+1)/2) and floor((n+1)/2) respectively.
For "lts" the default is floor(n/2) + floor((p+1)/2).
The "S" estimation method solves for the scale s
such that the average of a function chi of the residuals divided
by s is equal to a given constant.
The control argument is a list with components
psamp:the size of each sample. Defaults to p.
nsamp:the number of samples or "best" (the
default) or "exact" or "sample".
If "sample" the number chosen is min(5*p, 3000),
taken from Rousseeuw and Hubert (1997).
If "best" exhaustive enumeration is done up to 5000 samples;
if "exact" exhaustive enumeration will be attempted however
many samples are needed.
adjust:should the intercept be optimized for each
sample? Defaults to TRUE.
An object of class "lqs". This is a list with components
crit |
the value of the criterion for the best solution found, in
the case of |
sing |
character. A message about the number of samples which resulted in singular fits. |
coefficients |
of the fitted linear model |
bestone |
the indices of those points fitted by the best sample found (prior to adjustment of the intercept, if requested). |
fitted.values |
the fitted values. |
residuals |
the residuals. |
scale |
estimate(s) of the scale of the error. The first is based
on the fit criterion. The second (not present for |
There seems no reason other than historical to use the lms and
lqs options. LMS estimation is of low efficiency (converging
at rate n^{-1/3}) whereas LTS has the same asymptotic efficiency
as an M estimator with trimming at the quartiles (Marazzi, 1993, p.201).
LQS and LTS have the same maximal breakdown value of
(floor((n-p)/2) + 1)/n attained if
floor((n+p)/2) <= quantile <= floor((n+p+1)/2).
The only drawback mentioned of LTS is greater computation, as a sort
was thought to be required (Marazzi, 1993, p.201) but this is not
true as a partial sort can be used (and is used in this implementation).
Adjusting the intercept for each trial fit does need the residuals to
be sorted, and may be significant extra computation if n is large
and p small.
Opinions differ over the choice of psamp. Rousseeuw and Hubert
(1997) only consider p; Marazzi (1993) recommends p+1 and suggests
that more samples are better than adjustment for a given computational
limit.
The computations are exact for a model with just an intercept and adjustment, and for LQS for a model with an intercept plus one regressor and exhaustive search with adjustment. For all other cases the minimization is only known to be approximate.
P. J. Rousseeuw and A. M. Leroy (1987) Robust Regression and Outlier Detection. Wiley.
A. Marazzi (1993) Algorithms, Routines and S Functions for Robust Statistics. Wadsworth and Brooks/Cole.
P. Rousseeuw and M. Hubert (1997) Recent developments in PROGRESS. In L1-Statistical Procedures and Related Topics, ed Y. Dodge, IMS Lecture Notes volume 31, pp. 201–214.
## IGNORE_RDIFF_BEGIN set.seed(123) # make reproducible lqs(stack.loss ~ ., data = stackloss) lqs(stack.loss ~ ., data = stackloss, method = "S", nsamp = "exact") ## IGNORE_RDIFF_END
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