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_sigmasVector of length \(m\) containing the \(\log_{10}\) of the \(\sigma\)s
dim_normsVector of length \(m-1\) containing the average dimensionality \(\langle p \rangle\) for the respective kernel widths
optimal_sigmaMultiple local sigmas or the mean of the two global \(\sigma\)s around the highest \(\langle p \rangle\) (c(optimal_idx, optimal_idx+1L))
optimal_idxThe index of the highest \(\langle p \rangle\).
avrd_normsVector 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