Sboot_loccov {FRB} | R Documentation |
Calculates bootstrapped S-estimates using the Fast and Robust Bootstrap method.
Sboot_loccov(Y, R, ests = Sest_loccov(Y))
Y |
matrix or data frame |
R |
number of bootstrap samples |
ests |
original S-estimates as returned by Sest_loccov () |
This function is called by FRBpcaS
and FRBhotellingS
, it is typically not to be used on its own.
It requires the result of Sest_loccov
applied on Y
, supplied through the argument ests
.
If ests
is not provided, Sest_loccov
will be called with default arguments.
The fast and robust bootstrap was first developed by Salibian-Barrera and Zamar (2002) for univariate regression MM-estimators.
The value centered
gives a matrix with R
columns and p+p*p rows (p is the number of variables in Y
),
containing the recalculated estimates of the S-location and -covariance. Each column represents a different bootstrap sample.
The first p rows are the location estimates and the next p*p rows are the covariance estimates (vectorized). The estimates
are centered by the original estimates, which are also returned through Sest
.
A list containing:
centered |
recalculated estimates of location and covariance (centered by original estimates) |
Sest |
original estimates of location and covariance |
Gert Willems and Ella Roelant
FRBpcaS
, FRBhotellingS
, Sest_loccov
, MMboot_loccov
Y <- matrix(rnorm(50*5), ncol=5) Sests <- Sest_loccov(Y, bdp = 0.25) bootresult <- Sboot_loccov(Y, R = 1000, ests = Sests)