find_sigmas {destiny} | R Documentation

## Calculate the average dimensionality for m different gaussian kernel widths (σ).

### Description

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.

### Usage

    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)

### Arguments

`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 `...` have to be specified by name (e.g. `find_sigmas(data, verbose = FALSE)`)
`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

### Value

Object of class Sigmas

### See Also

`Sigmas`, the class returned by this; `DiffusionMap`, the class this is used for

### Examples

    data(guo)
    sigs <- find_sigmas(guo, verbose = TRUE)
    DiffusionMap(guo, sigs)

[Package destiny version 2.14.0 Index]