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madajz edited this page Sep 13, 2022
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Welcome to the bifurcatoR wiki! Here you will find the expanded methods section for the functions utilized in the bifurcatoR Shiny app (insert server link).
Deprecated.
This function is the workhorse for Module II of the Shiny app, which aims to estimate the power and false positive rate of detecting significant evidence against unimodality in a sample of size n. The inputs are, in order:
- n: the total sample size to sampled from the parent distribution
- alpha: the desired significance level/ type-I error rate. Note that any multiple testing corrections must be performed outside of this function
- nsim: the number of simulated samples of size n to be drawn. The higher this number, the more accurate the the power calculation, but the longer run time.
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dist: the distribution the simulated data is to be sample from. Currently the options are Gaussian and beta (soon Weibull).
- If dist == 'beta', the expected parameter list should contain the two shape parameters s1 and s2, where s1 = α and s2 = β
- A useful feature of the beta distribution, if both α and β are less than 1, the distribution is bimodal
- When dist = beta, the null distribution used to estimate the false positive rate comes from beta(α = 2, β = 2)
- if dist == 'norm', the expected parameter list should contain:
- __
- If dist == 'beta', the expected parameter list should contain the two shape parameters s1 and s2, where s1 = α and s2 = β