MMboottwosample {FRB} | R Documentation |
Calculates bootstrapped two sample MM-estimates using the Fast and Robust Bootstrap method.
MMboottwosample(X, groups, R, ests = twosampleMM(X, groups))
X |
matrix of data frame |
groups |
vector of 1's and 2's, indicating group numbers |
R |
number of bootstrap samples |
ests |
original MM-estimates as returned by twosampleMM () |
This function is called by FRBhotellingMM
, it is typically not to be used on its own.
It requires the result of twosampleMM
applied on X
, supplied through the argument ests
.
If ests
is not provided, twosampleMM
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 2*(2*p+p*p) rows (p is the number of variables in X
),
containing the recalculated estimates of the MM-locations, MM-shape, S-covariance and S-locations.
Each column represents a different bootstrap sample.
The first p rows are the MM-location estimates of the first sample, the next p rows are the MM-location estimates of the second sample,
the next p*p rows are the common MM-shape estimates (vectorized). Then the next
p*p rows are the common S-covariance estimates (vectorized), the next p are the S-location estimates of the first sample,
the final p rows are the S-location estimates of the second sample.
The estimates are centered by the original estimates, which are also returned through MMest
in vectorized form.
A list containing:
centered |
recalculated two sample MM- and S-estimates of location and scatter (centered by original estimates), see Details |
MMest |
original two sample MM- and S-estimates of location and scatter, see Details |
Ella Roelant and Gert Willems
See Also FRBhotellingMM
, twosampleMM
, Sboottwosample
Y1 <- matrix(rnorm(50*5), ncol=5) Y2 <- matrix(rnorm(50*5), ncol=5) Ybig <- rbind(Y1,Y2) grp <- c(rep(1,50),rep(2,50)) MMests <- twosampleMM(Ybig, grp) bootresult <- MMboottwosample(Ybig, grp, R=1000, ests=MMests)