DimensionReductionScatterplot: 'DimensionReductionScatterplot'

In Displayr/flipDimensionReduction: Algorithms for dimension reduction

DimensionReductionScatterplot

Description

Produces as 2-dimensional embedding. Takes either a data.frame of variables and optionally another vector to be used as a grouping variable, or takes a distance matrix.

Usage

DimensionReductionScatterplot(
  algorithm,
  data = NULL,
  data.groups = NULL,
  table = NULL,
  raw.table = FALSE,
  subset = NULL,
  perplexity = 10,
  binary = TRUE,
  normalization = FALSE,
  seed = 1066,
  ...
)

Arguments

algorithm	Valid options are "t-SNE", "MDS - Metric", "MDS - Non-metric", or "PCA". Note that 'PCA' does not accept a distance matrix as input.
data	A data.frame with cases by row and variables by column.
data.groups	A vector to be used as a grouping variable for the embedded data.
table	A symmetrical distance matrix or an object of class dist or DistanceMatrix.
raw.table	If TRUE, ParseUserEnteredTable is called on table to create a numeric matrix from text.
subset	A logical vector which describes the subset of data to be analyzed. Not used for table input.
perplexity	The perplexity coefficient which defines the extent of the locality of the dimension reduction. Used only when algorithm is t-SNE.
binary	If TRUE, unordered factors are converted to dummy variables. Otherwise, their value attributes/levels are used to coerce each factor to a single numeric variable. See the Details of AsNumeric.
normalization	If data is supplied, whether to standardize the data so each variable has a mean of 0 and standard deviation of 1.
seed	Random seed. Used only when algorithm is "t-SNE".
...	Other parameters passed to PrincipalComponentsAnalysis.

Details

For data input, all algorithms apart from PCA remove duplicated data and any case with NA is ignored by all algorithms.

Displayr/flipDimensionReduction documentation built on March 2, 2024, 3:50 a.m.