n_factors: Number of components/factors to retain in PCA/FA

View source: R/n_factors.R

n_factorsR Documentation

Number of components/factors to retain in PCA/FA


This function runs many existing procedures for determining how many factors to retain/extract from factor analysis (FA) or dimension reduction (PCA). It returns the number of factors based on the maximum consensus between methods. In case of ties, it will keep the simplest model and select the solution with the fewer factors.


  type = "FA",
  rotation = "varimax",
  algorithm = "default",
  package = c("nFactors", "psych"),
  cor = NULL,
  safe = TRUE,
  n_max = NULL,

  type = "PCA",
  rotation = "varimax",
  algorithm = "default",
  package = c("nFactors", "psych"),
  cor = NULL,
  safe = TRUE,



A data frame.


Can be "FA" or "PCA", depending on what you want to do.


Only used for VSS (Very Simple Structure criterion, see psych::VSS()). The rotation to apply. Can be "none", "varimax", "quartimax", "bentlerT", "equamax", "varimin", "geominT" and "bifactor" for orthogonal rotations, and "promax", "oblimin", "simplimax", "bentlerQ", "geominQ", "biquartimin" and "cluster" for oblique transformations.


Factoring method used by VSS. Can be "pa" for Principal Axis Factor Analysis, "minres" for minimum residual (OLS) factoring, "mle" for Maximum Likelihood FA and "pc" for Principal Components. "default" will select "minres" if type = "FA" and "pc" if type = "PCA".


Package from which respective methods are used. Can be "all" or a vector containing "nFactors", "psych", "PCDimension", "fit" or "EGAnet". Note that "fit" (which actually also relies on the psych package) and "EGAnet" can be very slow for bigger datasets. Thus, the default is c("nFactors", "psych"). You must have the respective packages installed for the methods to be used.


An optional correlation matrix that can be used (note that the data must still be passed as the first argument). If NULL, will compute it by running cor() on the passed data.


If TRUE, the function will run all the procedures in try blocks, and will only return those that work and silently skip the ones that may fail.


If set to a value (e.g., 10), will drop from the results all methods that suggest a higher number of components. The interpretation becomes 'from all the methods that suggested a number lower than n_max, the results are ...'.


Arguments passed to or from other methods.


n_components() is actually an alias for n_factors(), with different defaults for the function arguments.


A data frame.


There is also a plot()-method implemented in the see-package. n_components() is a convenient short-cut for n_factors(type = "PCA").


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n_factors(mtcars, type = "PCA")

result <- n_factors(mtcars[1:5], type = "FA")

# Setting package = 'all' will increase the number of methods (but is slow)
n_factors(mtcars, type = "PCA", package = "all")
n_factors(mtcars, type = "FA", algorithm = "mle", package = "all")

parameters documentation built on May 29, 2024, 8:49 a.m.