hc_kaisers_rule() determines the number of principal components to retain based on Kaiser's rule, which suggests keeping components with eigenvalues greater than 1. If the cumulative explained variance at this point is less than 80%, it can optionally suggest an alternative number of components that achieves at least 80% explained variance.
Usage
hc_kaisers_rule(AnnDatR, with_alternative = TRUE)
Arguments
- AnnDatR
An AnnDatR object containing the data with PCA results.
- with_alternative
Logical indicating whether to suggest an alternative number of components if the explained variance at Kaiser's rule is below 80% (default is TRUE).
Value
Number of principal components to retain based on Kaiser's rule.
Examples
# Determine number of components using Kaiser's rule
adata_res <- hc_pca(example_adata, components = 40)
n_components <- hc_kaisers_rule(adata_res)
#> [1] "Kaiser's rule is above 80% variation. Success"
print(n_components)
#> PC11
#> 11
