View source: R/empiricalBayesLM.R
bicovWeights  R Documentation 
Calculation of weights and the intermediate weight factors used in the calculation of biweight midcovariance and midcorrelation. The weights are designed such that outliers get smaller weights; the weights become zero for data points more than 9 median absolute deviations from the median.
bicovWeights( x, pearsonFallback = TRUE, maxPOutliers = 1, outlierReferenceWeight = 0.5625, defaultWeight = 0) bicovWeightFactors( x, pearsonFallback = TRUE, maxPOutliers = 1, outlierReferenceWeight = 0.5625, defaultFactor = NA) bicovWeightsFromFactors( u, defaultWeight = 0)
x 
A vector or a twodimensional array (matrix or data frame). If twodimensional, the weights will be calculated separately on each column. 
u 
A vector or matrix of weight factors, usually calculated by 
pearsonFallback 
Logical: if the median absolute deviation is zero, should standard deviation be substituted? 
maxPOutliers 
Optional specification of the maximum proportion of outliers, i.e., data with weights equal to

outlierReferenceWeight 
A number between 0 and 1 specifying what is to be considered an outlier when calculating the proportion of outliers. 
defaultWeight 
Value used for weights that correspond to a finite 
defaultFactor 
Value used for factors that correspond to a finite 
These functions are based on Equations (1) and (3) in Langfelder and Horvath (2012). The weight factor is denoted
u
in that article.
Langfelder and Horvath (2012) also describe the Pearson fallback and maximum proportion of outliers in detail. For a full discussion of the biweight midcovariance and midcorrelation, see Wilcox (2005).
A vector or matrix of the same dimensions as the input x
giving the bisquare weights
(bicovWeights
and bicovWeightsFromFactors
) or the bisquare factors
(bicovWeightFactors
).
Peter Langfelder
Langfelder P, Horvath S (2012) Fast R Functions for Robust Correlations and Hierarchical Clustering Journal of Statistical Software 46(11) 117 PMID: 23050260 PMCID: PMC3465711 Wilcox RR (2005). Introduction to Robust Estimation and Hypothesis Testing. 2nd edition. Academic Press, Section 9.3.8, page 399 as well as Section 3.12.1, page 83.
bicor
x = rnorm(100); x[1] = 10; plot(x, bicovWeights(x));
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