View source: R/MMboot_twosample.R
MMboot_twosample | R Documentation |
Calculates bootstrapped two sample MM-estimates using the Fast and Robust Bootstrap method.
MMboot_twosample(X, groups, R = 999, ests = MMest_twosample(X, groups))
X |
matrix of data frame. |
groups |
vector of 1's and 2's, indicating group numbers. |
R |
number of bootstrap samples. Default is |
ests |
original MM-estimates as returned by |
This function is called by FRBhotellingMM
, it is typically not to be used on its own.
It requires the result of MMest_twosample
applied on X
, supplied through the argument ests
.
If ests
is not provided, MMest_twosample
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 and extended to the two sample setting by Roelant et al. (2008).
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, Gert Willems and Stefan Van Aelst
E. Roelant, S. Van Aelst and G. Willems, (2008) Fast Bootstrap for Robust Hotelling Tests, COMPSTAT 2008: Proceedings in Computational Statistics (P. Brito, Ed.) Heidelberg: Physika-Verlag, 709–719.
M. Salibian-Barrera, S. Van Aelst and G. Willems (2008) Fast and robust bootstrap. Statistical Methods and Applications, 17, 41–71.
M. Salibian-Barrera, R.H. Zamar (2002) Bootstrapping robust estimates of regression. The Annals of Statistics, 30, 556–582.
S. Van Aelst and G. Willems (2013), Fast and robust bootstrap for multivariate inference: The R package FRB. Journal of Statistical Software, 53(3), 1–32. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.18637/jss.v053.i03")}.
See Also FRBhotellingMM
, Sboot_twosample
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 <- MMest_twosample(Ybig, grp)
bootresult <- MMboot_twosample(Ybig, grp, R=500, ests=MMests)
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