outlierPair {factDesign}R Documentation

A method for detecting single outliers in experimental designs with only two replicates per treatment condition.

Description

This function detects pairs of observations with unexpectedly large differences compared to the rest of the data. Pairs with large differences may include single outlier observations.

Usage

outlierPair(x, INDEX, p = 0.05, na.rm = TRUE)

Arguments

x A vector of observations.
INDEX A list of factors, each the same length as x, used to indicate the replicate observations.
p The significance level at which to perform the test.
na.rm If TRUE, will remove missing values.

Details

This outlier detection method is useful for small factorial designs in which the usual residuals from a linear model would have a large number of linear dependencies compared to the actual number of residuals. The function first calculates n difference between 2n replicates (call these pure residuals), and then constructs an F-statistic: f=(large squared p.r.)/((sum of remaining squared p.r.'s)/(n-1)). An p-value (adjusted for taking the largest of the p.r.'s) is calculated by n*Pr(F(1,n-1)>f). If f>=n-1, this p-value is exact, otherwise it is an upper bound.

Value

test Returns TRUE if an outlier pair is detected at the specified level of significance p.
pval The actual value of n*Pr(F(1,n-1)>f).
whichPair The index of the pair of observations with the largest difference.

Author(s)

Denise Scholtens

References

Scholtens et al. Analyzing Factorial Designed Microarray Experiments. Journal of Multivariate Analysis. To appear.

See Also

madOutPair

Examples


data(estrogen)

outlierPair(exprs(estrogen)[1,],INDEX=pData(estrogen),p=.05)
outlierPair(exprs(estrogen)[247,],INDEX=pData(estrogen),p=.05)
outlierPair(exprs(estrogen)[495,],INDEX=pData(estrogen),p=.05)






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