fsmeda.object: Description of 'fsmeda.object' Objects

In fsdaR: Robust Data Analysis Through Monitoring and Dynamic Visualization

Description of fsmeda.object Objects

Description

An object of class fsmeda.object holds information about the result of a call to fsmult when called with parameter monitoring=TRUE.

Value

The object itself is basically a list with the following components:

MAL	n x (n-init+1) matrix containing the monitoring of Each row represents the distance Mahalanobis distance for the corresponding unit.
BB	n x (n-init+1) matrix containing the information about the units belonging to the subset at each step of the forward search. The first column contains the indexes of the units forming subset in the initial step and each further column - the indexes of the units forming the corresponding step. The last column contains the units forming subset in the final step (all units).
md	n-by-1 vector containing the estimates of the robust Mahalanobis distances (in squared units). This vector contains the distances of each observation from the location of the data, relative to the scatter matrix cov.
mmd	(n-init) x 3 matrix. which contains the monitoring of minimum MD or (m+1)th ordered MD at each step of the forward search. 1st column = fwd search index (from init to n-1) 2nd column = minimum MD 3rd column = (m+1)th-ordered MD
msr	(n-init+1) x 3 matrix which contains the monitoring of maximum MD or m-th ordered MD at each step of the forward search. 1st column = fwd search index (from init to n) 2nd column = maximum MD 3rd column = mth-ordered MD
gap	(n-init+1) x 3 matrix which contains the monitoring of the gap (difference between minMD outside subset and max inside). 1st column = fwd search index (from init to n) 2nd column = min MD - max MD 3rd column = (m+1)th-ordered MD - mth ordered distance
Loc	(n-init+1) x (p+1) matrix which contains the monitoring of the estimated means at each step of the fwd search.
S2cov	(n-init+1) x (p*(p+1)/2+1) matrix which contains the monitoring of the of the elements of the covariance matrix in each step of the forward search. 1st column = fwd search index (from init to n) 2nd column = monitoring of S[1,1] 3rd column = monitoring of S[1,2] ... last column = monitoring of S[p,p]
detS	(n-init+1) x 2 matrix which contains the monitoring of the determinant of the covariance matrix in each step of the forward search.
Un	(n-init)-by-11 matrix which contains the unit(s) included in the subset at each step of the fwd search. REMARK: in every step the new subset is compared with the old subset. Un contains the unit(s) present in the new subset but not in the old one. Un[1 ,2] for example contains the unit included in step init+1. Un[end, 2] contains the units included in the final step of the search.
X	the data matrix X

The object has class "fsmeda".

Examples

## Not run:   
    data(hbk, package="robustbase")
    (out <- fsmult(hbk[,1:3], monitoring=TRUE))
    class(out)
    summary(out)

## End(Not run)

fsdaR documentation built on March 31, 2023, 8:18 p.m.