bicovWeights: Weights used in biweight midcovariance

Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/empiricalBayesLM.R

Description

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.

Usage

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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)

Arguments

x

A vector or a two-dimensional array (matrix or data frame). If two-dimensional, the weights will be calculated separately on each column.

u

A vector or matrix of weight factors, usually calculated by bicovWeightFactors.

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 below.

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 x but the weights themselves would not be finite, for example, when a column in x is constant.

defaultFactor

Value used for factors that correspond to a finite x but the weights themselves would not be finite, for example, when a column in x is constant.

Details

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).

Value

A vector or matrix of the same dimensions as the input x giving the bisquare weights (bicovWeights and bicovWeightsFromFactors) or the bisquare factors (bicovWeightFactors).

Author(s)

Peter Langfelder

References

Langfelder P, Horvath S (2012) Fast R Functions for Robust Correlations and Hierarchical Clustering Journal of Statistical Software 46(11) 1-17 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.

See Also

bicor

Examples

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x = rnorm(100);
x[1] = 10;
plot(x, bicovWeights(x));

nosarcasm/WGCNA documentation built on May 28, 2019, 1:01 p.m.