Description Usage Arguments Details Value Author(s) References Examples
The function determines a robust subsample and computes estimates of location and scatter on the subset.
1 |
data |
data set to be analyzed, at least a 2-dimensional matrix whose number of rows (i.e. observations |
N |
Size of the (robust) subsample to be determined. Default is |
lmax |
Numerical option: determines the maximal number of pruning steps, see deteils. |
The function uses the minimum.spanning.tree
function from the igraph-package to determine the minimum spanning tree (MST) of the data. The resulting MST is iteratively pruned by deleting edges (starting with the longest edge in the MST) until a connected subset with sufficient size (N
) remains. Based on the robust subsample, location and scatter are estimated.
loc |
Location estimate based on the robust subsample. |
cov |
Covariance estimate based on the robust subsample. |
sample |
Index of the observations in the robust subsample. |
data |
The input data set. |
Thomas Kirschstein <thomas.kirschstein@wiwi.uni-halle.de>
Kirschstein, T., Liebscher, S., and Becker, C. (2013): Robust estimation of location and scatter by pruning the minimum spanning tree, Journal of Multivariate Analysis, 120, 173-184, DOI: 10.1016/j.jmva.2013.05.004.
Liebscher, S., Kirschstein, T. (2015): Efficiency of the pMST and RDELA Location and Scatter Estimators, AStA-Advances in Statistical Analysis, 99(1), 63-82, DOI: 10.1007/s10182-014-0231-7.
1 2 3 4 | # Determine subsample of minimal size
# sub <- pMST(halle)
# Determine subsample of size=900
# extsub <- pMST(halle, N=900)
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