reduceDimensionality: Reduce dimensionality after processing missing values from...

In nuno-agostinho/psichomics: Graphical Interface for Alternative Splicing Quantification, Analysis and Visualisation

Reduce dimensionality after processing missing values from data frame

Description

Reduce dimensionality after processing missing values from data frame

Usage

reduceDimensionality(
  data,
  type = c("pca", "ica"),
  center = TRUE,
  scale. = FALSE,
  naTolerance = NULL,
  missingValues = round(0.05 * ncol(data)),
  ...
)

Arguments

data	Data frame: data
type	Character: dimensionality reduction technique (pca or ica)
center	either a logical value or numeric-alike vector of length equal to the number of columns of x, where ‘numeric-alike’ means that as.numeric(.) will be applied successfully if is.numeric(.) is not true.
scale.	Boolean: scale variables?
naTolerance	Integer: percentage of tolerated missing values per column (deprecated)
missingValues	Integer: number of tolerated missing values per column to be replaced with the mean of the values of that same column
...	Extra parameters passed to FUN

Value

PCA result in a prcomp object or ICA result object

nuno-agostinho/psichomics documentation built on Feb. 11, 2024, 11:16 p.m.