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

In psichomics: Graphical Interface for Alternative Splicing Quantification, Analysis and Visualisation

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

psichomics documentation built on Nov. 8, 2020, 5:44 p.m.