find_sigmas {destiny} | R Documentation |
The sigma with the maximum value in average dimensionality is close to the ideal one. Increasing step number gets this nearer to the ideal one.
find_sigmas( data, step_size = 0.1, steps = 10L, start = NULL, sample_rows = 500L, early_exit = FALSE, ..., censor_val = NULL, censor_range = NULL, missing_range = NULL, vars = NULL, verbose = TRUE )
data |
Data set with n observations. Can be a data.frame, matrix, ExpressionSet or SingleCellExperiment. |
step_size |
Size of log-sigma steps |
steps |
Number of steps/calculations |
start |
Initial value to search from. (Optional. default: \log_{10}(min(dist(data)))) |
sample_rows |
Number of random rows to use for sigma estimation or vector of row indices/names to use. In the first case, only used if actually smaller than the number of available rows (Optional. default: 500) |
early_exit |
logical. If TRUE, return if the first local maximum is found, else keep running |
... |
Unused. All parameters to the right of the |
censor_val |
Value regarded as uncertain. Either a single value or one for every dimension |
censor_range |
Uncertainity range for censoring. A length-2-vector of certainty range start and end. TODO: also allow 2\times G matrix |
missing_range |
Whole data range for missing value model. Has to be specified if NAs are in the data |
vars |
Variables (columns) of the data to use. Specifying TRUE will select all columns (default: All floating point value columns) |
verbose |
logical. If TRUE, show a progress bar and plot the output |
Object of class Sigmas
Sigmas
, the class returned by this; DiffusionMap
, the class this is used for
data(guo) sigs <- find_sigmas(guo, verbose = TRUE) DiffusionMap(guo, sigs)