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Tests the accumulated quality scores for outliers using cosine similarity

cosine.similarity.iterative.Rd

This function takes quality.scores, trims it and fits it to the distribution given. It then iteratively tests the largest datapoint compared a null distribution of size no.simulations. If the largest datapoint has a significant p-value it tests the 2nd largest one and so on. The function supports the following distributions:

  • 'weibull'

  • 'norm'

  • 'gamma'

  • 'exp'

  • 'lnorm'

  • 'cauchy'

  • 'logis'

Usage

cosine.similarity.iterative(
  quality.scores,
  no.simulations,
  distribution = c("lnorm", "weibull", "norm", "gamma", "exp", "cauchy", "logis"),
  trim.factor = 0.05,
  alpha.significant = 0.05
)

Arguments

quality.scores

A dataframe with columns 'Sum' (of scores) and 'Sample', i.e. the output of accumulate.zscores

no.simulations

The number of datasets to simulate

distribution

A distribution to test, will default to 'lnorm'

trim.factor

What fraction of values of each to trim to get parameters without using extremes

alpha.significant

Alpha value for significance