Holds the information about how the sigma
parameter for a DiffusionMap was obtained,
and in this way provides a plotting function for the find_sigmas heuristic.
You should not need to create a Sigmas object yourself. Provide sigma
to DiffusionMap instead or use find_sigmas.
Sigmas(...) # S4 method for Sigmas optimal_sigma(object) # S4 method for Sigmas print(x) # S4 method for Sigmas show(object)
object, x | Sigmas object |
---|---|
... | See “Slots” below |
Sigmas
creates an object of the same class
optimal_sigma
retrieves the numeric value of the optimal sigma or local sigmas
A Sigmas object is either created by find_sigmas or by specifying the sigma
parameter to DiffusionMap.
In the second case, if the sigma
parameter is just a number,
the resulting Sigmas
object has all slots except of optimal_sigma
set to NULL
.
log_sigmas
Vector of length \(m\) containing the \(\log_{10}\) of the \(\sigma\)s
dim_norms
Vector of length \(m-1\) containing the average dimensionality \(\langle p \rangle\) for the respective kernel widths
optimal_sigma
Multiple local sigmas or the mean of the two global \(\sigma\)s around the highest \(\langle p \rangle\) (c(optimal_idx, optimal_idx+1L)
)
optimal_idx
The index of the highest \(\langle p \rangle\).
avrd_norms
Vector of length \(m\) containing the average dimensionality for the corresponding sigma.
find_sigmas
, the function to determine a locally optimal sigma and returning this class
#> [1] 7.663719print(sigs)#> Sigmas (10 Steps performed) #> optimal_sigma: num 7.66