Calculate an outlier cutoff using cosine similarity

This function takes quality.scores, trims it and fits it to the distribution given. It then simulates as many datasets as stated by no.simulations, and computes the cosine similarity of each dataset against theoretical distribution. It uses what would correspond to a significant value to then calculate what observed value this would correspond to. The function supports the following distributions:

• 'weibull'

• 'norm'

• 'gamma'

• 'exp'

• 'lnorm'

• 'cauchy'

• 'logis'

Usage

cosine.similarity.cutoff(
  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