lqs(x, ...)
lqs.formula(formula, data = NULL, ...,
method = c("lts", "lqs", "lms", "S", "model.frame"),
subset, na.action = na.fail, model = TRUE,
x = FALSE, y = FALSE, contrasts = NULL)
lqs.default(x, y, intercept, method = c("lts", "lqs", "lms", "S"),
quantile, control = lqs.control(...), k0 = 1.548, seed, ...)
lmsreg(...)
ltsreg(...)
print.lqs(x, digits, ...)
residuals.lqs(x)
formula
|
a formula of the form y ~ x1 + x2 + ...{}{}.
|
data
|
data frame from which variables specified in formula are
preferentially to be taken.
|
subset
| An index vector specifying the cases to be used in fitting. (NOTE: If given, this argument must be named exactly.) |
na.action
|
A function to specify the action to be taken if NAs are found. The
default action is for the procedure to fail. An alternative is
na.omit, which leads to omission of cases with missing values on any
required variable. (NOTE: If given, this argument must be named
exactly.)
|
x
| a matrix or data frame containing the explanatory variables. |
y
|
the response: a vector of length the number of rows of x.
|
intercept
| should the model include an intercept? |
method
|
the method to be used. model.frame returns the model frame: for the
others see the Details section. Using lmsreg or
ltsreg forces "lms" and "lts" respectively.
|
quantile
|
the quantile to be used: see Details. This is over-ridden if
method = "lms".
|
control
|
additional control items: see Details.
|
seed
|
the seed to be used for random sampling: see .Random.seed. The
current value of .Random.seed will be preserved if it is set..
|
...
|
arguments to be passed to lqs.default or lqs.control.
|
good points in the dataset, thereby
achieving a regression estimator with a high breakdown point.
lmsreg and ltsreg are compatibility wrappers.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
item{psamp}{
the size of each sample. Defaults to p.
}
item{nsamp}{
the number of samples or "best" 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.
}
item{adjust}{
should the intercept be optimized for each sample?
}
"lqs".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.
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.
predict.lqsdata(stackloss) .Random.seed <- 1:4 lqs(stack.loss ~ ., data=stackloss) lqs(stack.loss ~ ., data=stackloss, method="S", nsamp="exact")