EFA: Principal Component Analysis (PCA) and Exploratory Factor...
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EFA	R Documentation

Principal Component Analysis (PCA) and Exploratory Factor analysis (EFA).

Description

An extension of psych::principal() and psych::fa(), performing either Principal Component Analysis (PCA) or Exploratory Factor Analysis (EFA).

Three options to specify variables:

var + items: use the common and unique parts of variable names.
vars: directly define a character vector of variables.
varrange: use the starting and stopping positions of variables.

Usage

EFA(
  data,
  var,
  items,
  vars = NULL,
  varrange = NULL,
  rev = NULL,
  method = c("pca", "pa", "ml", "minres", "uls", "ols", "wls", "gls", "alpha"),
  rotation = c("none", "varimax", "oblimin", "promax", "quartimax", "equamax"),
  nfactors = c("eigen", "parallel", "(any number >= 1)"),
  sort.loadings = TRUE,
  hide.loadings = 0,
  plot.scree = TRUE,
  kaiser = TRUE,
  max.iter = 25,
  min.eigen = 1,
  digits = 3,
  nsmall = digits,
  file = NULL
)

PCA(..., method = "pca")

Arguments

`data`	Data frame.
`var`	[Option 1] The common part across the variables. e.g., `"RSES"`
`items`	[Option 1] The unique part across the variables. e.g., `1:10`
`vars`	[Option 2] A character vector specifying the variables. e.g., `c("X1", "X2", "X3", "X4", "X5")`
`varrange`	[Option 3] A character string specifying the positions ("starting:stopping") of variables. e.g., `"A1:E5"`
`rev`	[Optional] Variables that need to be reversed. It can be (1) a character vector specifying the reverse-scoring variables (recommended), or (2) a numeric vector specifying the item number of reverse-scoring variables (not recommended).
`method`	Extraction method. `"pca"` - Principal Component Analysis (default) `"pa"` - Principal Axis Factor Analysis `"ml"` - Maximum Likelihood Factor Analysis `"minres"` - Minimum Residual Factor Analysis `"uls"` - Unweighted Least Squares Factor Analysis `"ols"` - Ordinary Least Squares Factor Analysis `"wls"` - Weighted Least Squares Factor Analysis `"gls"` - Generalized Least Squares Factor Analysis `"alpha"` - Alpha Factor Analysis (Kaiser & Coffey, 1965)
`rotation`	Rotation method. `"none"` - None (not suggested) `"varimax"` - Varimax (default) `"oblimin"` - Direct Oblimin `"promax"` - Promax `"quartimax"` - Quartimax `"equamax"` - Equamax
`nfactors`	How to determine the number of factors/components? `"eigen"` - based on eigenvalue (> minimum eigenvalue) (default) `"parallel"` - based on parallel analysis (any number >= 1) - user-defined fixed number
`sort.loadings`	Sort factor/component loadings by size? Default is `TRUE`.
`hide.loadings`	A number (0~1) for hiding absolute factor/component loadings below this value. Default is `0` (does not hide any loading).
`plot.scree`	Display the scree plot? Default is `TRUE`.
`kaiser`	Do the Kaiser normalization (as in SPSS)? Default is `TRUE`.
`max.iter`	Maximum number of iterations for convergence. Default is `25` (the same as in SPSS).
`min.eigen`	Minimum eigenvalue (used if `nfactors="eigen"`). Default is `1`.
`digits, nsmall`	Number of decimal places of output. Default is `3`.
`file`	File name of MS Word (`.doc`).
`...`	Arguments passed from `PCA()` to `EFA()`.

Value

A list of results:

result: The R object returned from psych::principal() or psych::fa()
result.kaiser: The R object returned from psych::kaiser() (if any)
extraction.method: Extraction method
rotation.method: Rotation method
eigenvalues: A data.frame of eigenvalues and sum of squared (SS) loadings
loadings: A data.frame of factor/component loadings and communalities
scree.plot: A ggplot2 object of the scree plot

Functions

EFA(): Exploratory Factor Analysis
PCA(): Principal Component Analysis - a wrapper of EFA(..., method="pca")

Note

Results based on the varimax rotation method are identical to SPSS. The other rotation methods may produce results slightly different from SPSS.

Examples

data = psych::bfi
EFA(data, "E", 1:5)              # var + items
EFA(data, "E", 1:5, nfactors=2)  # var + items

EFA(data, varrange="A1:O5",
    nfactors="parallel",
    hide.loadings=0.45)

# the same as above:
# using dplyr::select() and dplyr::matches()
# to select variables whose names end with numbers
# (regexp: \d matches all numbers, $ matches the end of a string)
data %>% select(matches("\\d$")) %>%
  EFA(vars=names(.),       # all selected variables
      method="pca",        # default
      rotation="varimax",  # default
      nfactors="parallel", # parallel analysis
      hide.loadings=0.45)  # hide loadings < 0.45

bruceR documentation built on March 7, 2023, 5:41 p.m.