exceptionalScore: exceptionalScore

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

View source: R/exceptionalScore.R

Description

This function can be used to detect exceptionally high or low scores in a vector.

Usage

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exceptionalScore(x, prob = 0.025, both = TRUE, silent = FALSE,
                 quantileCorrection = 1e-04, quantileType = 8)

Arguments

x

Vector in which to detect exceptional scores.

prob

Probability that a score is exceptionally positive or negative; i.e. scores with a quartile lower than prob or higher than 1-prob are considered exceptional (if both is TRUE, at least). So, note that a prob of .025 means that if both=TRUE, the most exceptional 5% of the values is marked as such.

both

Whether to consider values exceptional if they're below prob as well as above 1-prob, or whether to only consider values exceptional if they're below prob is prob is < .5, or above prob if prob > .5.

silent

Can be used to suppress messages.

quantileCorrection

By how much to correct the computed quantiles; this is used because when a distribution is very right-skewed, the lowest quantile is the lowest value, which is then also the mode; without subtracting a correction, almost all values would be marked as 'exceptional'.

quantileType

The algorithm used to compute the quantiles; see quantile.

Details

Note that of course, by definition, prob of 2*prob percent of the values is exceptional, so it is usually not a wise idea to remove scores based on their 'exceptionalness'. Instead, use exceptionalScores, which calls this function, to see how often participants answered exceptionally, and remove them based on that.

Value

A logical vector, indicating for each value in the supplied vector whether it is exceptional.

Author(s)

Gjalt-Jorn Peters

Maintainer: Gjalt-Jorn Peters <gjalt-jorn@userfriendlyscience.com>

See Also

quantile, exceptionalScores

Examples

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exceptionalScore(c(1,1,2,2,2,3,3,3,4,4,4,5,5,5,5,6,6,7,8,20), prob=.05);

userfriendlyscience documentation built on May 2, 2019, 1:09 p.m.