LXS_control {fsdaR}

Creates an LSX_control object

Description

Creates an object of class LXS_control to be used with the fsreg() function, containing various control parameters.

Usage

    LXS_control(intercept = TRUE, lms, h, bdp, nsamp, rew = FALSE, conflev = 0, 
        msg = TRUE, nocheck = FALSE, nomes = FALSE, plot = FALSE)

Arguments

intercept

Indicator for constant term. Scalar. If intercept=TRUE, a model with constant term will be fitted (default), else, no constant term will be included.

lms

Criterion to use to find the initial subset to initialize the search (LMS, LTS with concentration steps, LTS without concentration steps or subset supplied directly by the user). The default value is 1 (Least Median of Squares is computed to initialize the search). On the other hand, if the user wants to initialze the search with LTS with all the default options for concentration steps then lms=2. If the user wants to use LTS without concentration steps, lms can be a scalar different from 1 or 2. If lms is a list it is possible to control a series of options for concentration steps (for more details see option lms inside LXS_control). If, on the other hand, the user wants to initialize the search with a prespecified set of units there are two possibilities:

1. lms can be a vector with length greater than 1 which contains the list of units forming the initial subset. For example, if the user wants to initialize the search with units 4, 6 and 10 then lms=c(4, 6, 10);

2. lms is a struct which contains a field named bsb which contains the list of units to initialize the search. For example, in the case of simple regression through the origin with just one explanatory variable, if the user wants to initialize the search with unit 3 then lms=list(bsb=3).

h

The number of observations that have determined the least trimmed squares estimator, scalar. h is an integer greater or equal than p but smaller then n. Generally if the purpose is outlier detection h=[0.5*(n+p+1)] (default value). h can be smaller than this threshold if the purpose is to find subgroups of homogeneous observations. In this function the LTS/LMS estimator is used just to initialize the search.

bdp

Breakdown point. It measures the fraction of outliers the algorithm should resist. In this case any value greater than 0 but smaller or equal than 0.5 will do fine. If on the other hand the purpose is subgroups detection then bdp can be greater than 0.5. In any case however n*(1-bdp) must be greater than p. If this condition is not fulfilled an error will be given. Please specify h or bdp not both.

nsamp

Number of subsamples which will be extracted to find the robust estimator, scalar. If nsamp=0 all subsets will be extracted. They will be (n choose p). If the number of all possible subset is <1000 the default is to extract all subsets otherwise just 1000.

rew

LXS reweighted - if rew=1 the reweighted version of LTS (LMS) is used and the output quantities refer to the reweighted version else no reweighting is performed (default).

conflev

Confidence level which is used to declare units as outliers, usually conflev=0.95, 0.975, 0.99 (individual alpha) or 1-0.05/n, 1-0.025/n, 1-0.01/n (simultaneous alpha). Default value is 0.975.

msg

Controls whether to display or not messages on the screen If msg==1 (default) messages are displayed on the screen about step in which signal took place else no message is displayed on the screen.

nocheck

Check input arguments, scalar. If nocheck=TRUE no check is performed on matrix y and matrix X. Notice that y and X are left unchanged. In other words the additional column of ones for the intercept is not added. As default nocheck=FALSE.

nomes

It controls whether to display or not on the screen messages about estimated time to compute LMS (LTS). If nomes is equal to 1 no message about estimated time to compute LMS (LTS) is displayed, else if nomes is equal to 0 (default), a message about estimated time is displayed.

plot

Plot on the screen. Scalar. If plots=TRUE the plot of minimum deletion residual with envelopes based on n observations and the scatterplot matrix with the outliers highlighted is produced. If plots=2 the user can also monitor the intermediate plots based on envelope superimposition. If plots=FALSE (default) no plot is produced.

Details

Creates an object of class FSR_control to be used with the fsreg() function, containing various control parameters.

Value

An object of class "LXS_control" which is basically a list with components the input arguments of the function mapped accordingly to the corresponding Matlab function.

Author(s)

FSDA team

See Also

See Also as Sreg_control, MMreg_control and FSR_control

Examples

## Not run:   
data(hbk, package="robustbase")
(out <- fsreg(Y~., data=hbk, method="LMS", control=LXS_control(h=56, nsamp=500, lms=2)))

## End(Not run)