run_efa  R Documentation 
This function is intended for use on independent samples rather than integrated with kfold crossvalidation.
run_efa( variables, m = floor(ncol(variables)/4), rotation = "oblimin", simple = TRUE, threshold = NA, single.item = c("keep", "drop", "none"), identified = TRUE, constrain0 = FALSE, ordered = FALSE, estimator = NULL, missing = "listwise", ... )
variables 
a 
m 
integer; maximum number of factors to extract. Default is 4 items per factor. 
rotation 
character (casesensitive); any rotation method listed in

simple 
logical; Should the most simple structure be returned (default)?
If 
threshold 
numeric between 0 and 1 indicating the minimum (absolute) value of the loading for an item on a factor. 
single.item 
character indicating how singleitem factors should be treated.
Use 
identified 
logical; Should identification check for rotational uniqueness a la Millsap (2001) be performed? 
constrain0 
logical; Should variable(s) with all loadings below 
ordered 
logical; Should items be treated as ordinal and the
polychoric correlations used in the factor analysis? When 
estimator 
if 
missing 
default is "listwise". See 
... 
other arguments passed to 
A threeelement list
:
efas lavaan
object for each m model
loadings (rotated) factor loading matrix for each m model
cfa.syntax CFA syntax generated from loadings
# simulate data based on a 3factor model with standardized loadings sim.mod < "f1 =~ .7*x1 + .8*x2 + .3*x3 + .7*x4 + .6*x5 + .8*x6 + .4*x7 f2 =~ .8*x8 + .7*x9 + .6*x10 + .5*x11 + .5*x12 + .7*x13 + .6*x14 f3 =~ .6*x15 + .5*x16 + .9*x17 + .4*x18 + .7*x19 + .5*x20 f1 ~~ .2*f2 f2 ~~ .2*f3 f1 ~~ .2*f3 x9 ~~ .2*x10" set.seed(1161) sim.data < simstandard::sim_standardized(sim.mod, n = 900, latent = FALSE, errors = FALSE)[c(2:9,1,10:20)] # Run 1, 2, and 3factor models efas < run_efa(sim.data, m = 3)
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