EFA {bruceR}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:

  1. var + items: use the common and unique parts of variable names.

  2. vars: directly define a character vector of variables.

  3. 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

Note

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

See Also

MEAN, Alpha, CFA

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


[Package bruceR version 0.8.0 Index]