| fsmeda.object {fsdaR} | R Documentation |
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)