# lqs

##### 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.

##### Usage

`lqs(x, ...)`## S3 method for class 'formula':
lqs(formula, data, \dots,
method = c("lts", "lqs", "lms", "S", "model.frame"),
subset, na.action, model = TRUE,
x.ret = FALSE, y.ret = FALSE, contrasts = NULL)

## S3 method for class 'default':
lqs(x, y, intercept = TRUE, method = c("lts", "lqs", "lms", "S"),
quantile, control = lqs.control(...), k0 = 1.548, seed, ...)

lmsreg(...)
ltsreg(...)

##### Arguments

- 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
- function to specify the action to be taken if
`NA`

s are found. The default action is for the procedure to fail. Alternatives include`na.omit`

and - model, x.ret, y.ret
- logical. If
`TRUE`

the model frame, the model matrix and the response are returned, respectively. - contrasts
- an optional list. See the
`contrasts.arg`

of`model.matrix.default`

. - 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`

. - k0
- the cutoff / tuning constant used for $\chi()$
and $\psi()$ functions when
`method = "S"`

, currently corresponding to Tukey'sbiweight . - 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`

, see`control`

above and`Details`

.

##### Details

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
[object Object],[object Object],[object Object]

##### Value

- 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 `method == "S"`

before IWLS refinement.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 `method == "S"`

) is based on the variance of those residuals whose absolute value is less than 2.5 times the initial estimate.

##### Note

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.
```

##### References

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.

##### See Also

##### Examples

```
set.seed(123) # make reproducible
lqs(stack.loss ~ ., data = stackloss)
lqs(stack.loss ~ ., data = stackloss, method = "S", nsamp = "exact")
```

*Documentation reproduced from package MASS, version 7.3-0, License: GPL-2 | GPL-3*