efa: Exploratory Factor Analysis

In jmv: The 'jamovi' Analyses

Exploratory Factor Analysis

Description

Exploratory Factor Analysis

Usage

efa(data, vars, nFactorMethod = "parallel", nFactors = 1,
  minEigen = 0, extraction = "minres", rotation = "oblimin",
  hideLoadings = 0.3, sortLoadings = FALSE, screePlot = FALSE,
  eigen = FALSE, factorCor = FALSE, factorSummary = FALSE,
  modelFit = FALSE, kmo = FALSE, bartlett = FALSE,
  factorScoreMethod = "Thurstone")

Arguments

data	the data as a data frame
vars	a vector of strings naming the variables of interest in data
nFactorMethod	'parallel' (default), 'eigen' or 'fixed', the way to determine the number of factors
nFactors	an integer (default: 1), the number of factors in the model
minEigen	a number (default: 0), the minimal eigenvalue for a factor to be included in the model
extraction	'minres' (default), 'ml', or 'pa' use respectively 'minimum residual', 'maximum likelihood', or 'prinicipal axis' as the factor extraction method
rotation	'none', 'varimax', 'quartimax', 'promax', 'oblimin' (default), or 'simplimax', the rotation to use in estimation
hideLoadings	a number (default: 0.3), hide factor loadings below this value
sortLoadings	TRUE or FALSE (default), sort the factor loadings by size
screePlot	TRUE or FALSE (default), show scree plot
eigen	TRUE or FALSE (default), show eigenvalue table
factorCor	TRUE or FALSE (default), show inter-factor correlations
factorSummary	TRUE or FALSE (default), show factor summary
modelFit	TRUE or FALSE (default), show model fit measures and test
kmo	TRUE or FALSE (default), show Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy (MSA) results
bartlett	TRUE or FALSE (default), show Bartlett's test of sphericity results
factorScoreMethod	'Thurstone' (default), 'Bartlett', 'tenBerge', 'Anderson', or 'Harman' use respectively 'Thurstone', 'Bartlett', 'ten Berge', 'Anderson & Rubin', or 'Harman' method for estimating factor scores

Value

A results object containing:

results$text					a preformatted

Examples

data('iris')

efa(iris, vars = vars(Sepal.Length, Sepal.Width, Petal.Length, Petal.Width))

#
#  EXPLORATORY FACTOR ANALYSIS
#
#  Factor Loadings
#  ------------------------------------------------
#                    1        2        Uniqueness
#  ------------------------------------------------
#    Sepal.Length    0.993                0.10181
#    Sepal.Width              0.725       0.42199
#    Petal.Length    0.933                0.00483
#    Petal.Width     0.897                0.07088
#  ------------------------------------------------
#    Note. 'oblimin' rotation was used
#

jmv documentation built on June 22, 2024, 10:40 a.m.