n_factors  R Documentation 
This function runs many existing procedures for determining how many factors to retain/extract from factor analysis (FA) or dimension reduction (PCA). It returns the number of factors based on the maximum consensus between methods. In case of ties, it will keep the simplest model and select the solution with the fewer factors.
n_factors(
x,
type = "FA",
rotation = "varimax",
algorithm = "default",
package = c("nFactors", "psych"),
cor = NULL,
safe = TRUE,
n_max = NULL,
...
)
n_components(
x,
type = "PCA",
rotation = "varimax",
algorithm = "default",
package = c("nFactors", "psych"),
cor = NULL,
safe = TRUE,
...
)
x 
A data frame. 
type 
Can be 
rotation 
Only used for VSS (Very Simple Structure criterion, see

algorithm 
Factoring method used by VSS. Can be 
package 
Package from which respective methods are used. Can be

cor 
An optional correlation matrix that can be used (note that the
data must still be passed as the first argument). If 
safe 
If 
n_max 
If set to a value (e.g., 
... 
Arguments passed to or from other methods. 
n_components()
is actually an alias for n_factors()
, with
different defaults for the function arguments.
A data frame.
There is also a
plot()
method
implemented in the seepackage.
n_components()
is a convenient shortcut for n_factors(type = "PCA")
.
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Finch, W. H. (2019). Using Fit Statistic Differences to Determine the Optimal Number of Factors to Retain in an Exploratory Factor Analysis. Educational and Psychological Measurement.
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Revelle, W., & Rocklin, T. (1979). Very simple structure: An alternative procedure for estimating the optimal number of interpretable factors. Multivariate Behavioral Research, 14(4), 403414.
Velicer, W. F. (1976). Determining the number of components from the matrix of partial correlations. Psychometrika, 41(3), 321327.
library(parameters)
n_factors(mtcars, type = "PCA")
result < n_factors(mtcars[1:5], type = "FA")
as.data.frame(result)
summary(result)
# Setting package = 'all' will increase the number of methods (but is slow)
n_factors(mtcars, type = "PCA", package = "all")
n_factors(mtcars, type = "FA", algorithm = "mle", package = "all")
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