jmv (version 2.5.6)

efa: 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")

Value

A results object containing:

results$text

a preformatted

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

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
#