The kurtosis cutoff is a high quantile (default 0.99) of the sampling distribution of kurtosis for Normal iid data of the same length as the components; it is estimated by simulation or calculated from the theoretical asymptotic distribution if the components are long enough.
high_kurtosis(Comps, kurt_quantile = 0.99, n_sim = 5000, min_1 = FALSE)| Comps | A matrix; each column is a component. For PCA, this is the U matrix. For ICA, this is the M matrix. |
|---|---|
| kurt_quantile | components with kurtosis of at least this quantile are kept. |
| n_sim | The number of simulation data to use for estimating the sampling distribution of kurtosis. Only used if a new simulation is performed. (If \(n<1000\) and the quantile is 90%, a pre-computed value is used instead. If \(n>1000\), the theoretical asymptotic distribution is used instead.) |
| min_1 | Require at least one component to be selected? In other words, if
no components meet the quantile cutoff, should the component with the highest
kurtosis be returned? Default: |