nest {Rnest} | R Documentation |
Next Eigenvalue Sufficiency Test (NEST)
Description
nest
is used to identify the number of factors to retain in exploratory factor analysis.
Usage
nest(
.data,
...,
n = NULL,
nreps = 1000,
alpha = 0.05,
max.fact = NULL,
method = "ml",
missing = "fiml",
cluster = NULL,
ordered = NULL
)
Arguments
.data |
a data frame, a numeric matrix, covariance matrix or correlation matrix from which to determine the number of factors. |
... |
arguments for |
n |
the number of cases (subjects, participants, or units) if a covariance matrix is supplied in |
nreps |
the number of replications to derive the empirical probability distribution of each eigenvalue. Default is 1000. |
alpha |
a vector of type I error rates or |
max.fact |
an optional maximum number of factor to extract. Default is |
method |
a method used to compute loadings and uniquenesses. Four methods are implemented in |
missing |
how should missing data be removed. |
cluster |
a (single) variable name in the data frame defining the clusters in a two-level dataset. |
ordered |
a character vector to treat the variables as ordered (ordinal) variables. If TRUE, all observed endogenous variables are treated as ordered (ordinal). |
Details
The Next Eigenvalues Sufficiency Test (NEST) is an extension of parallel analysis by adding a sequential hypothesis testing procedure for every k = 0, ..., \code{max.fact}
factor until the hypothesis is not rejected.
At k = 0
, NEST and parallel analysis are identical. Both use an identity matrix as the correlation matrix. Once the first hypothesis is rejected, NEST uses a correlation matrix based on the loadings and uniquenesses of the k^{th}
factorial structure. NEST then resamples nreps
times the k^{th}
eigenvalue of this new correlation matrix. NEST stops when the k^{th}
eigenvalues is below the 1-\alpha
*100
There is four method
already implemented in nest
to estimate loadings and uniquenesses: maximum likelihood ("ml"
; default), principal axis factoring ("paf"
), regularized common factor analysis method = "rcfa"
, and minimum rank factor analysis ("mrfa"
). These functions use as arguments: covmat
, n
, factors
, and ...
(supplementary arguments passed by nest
). They return loadings
and uniquenesses
. Any other user-defined functions can be used as long as it is programmed likewise.
The method = "paf"
is the same as Achim's (2017) NESTip.
Value
nest()
returns an object of class nest
. The functions summary
and plot
are used to obtain and show a summary of the results.
An object of class nest
is a list containing the following components:
-
nfactors
- The number of factors to retains (one byalpha
). -
cor
- The supplied correlation matrix. -
n
- The number of cases (subjects, participants, or units). -
values
- The eigenvalues of the supplied correlation matrix. -
alpha
- The type I error rate. -
method
- The method used to compute loadings and uniquenesses. -
nreps
- The number of replications used. -
prob
- Probabilities of each factor. -
Eig
- A list of simulated eigenvalues.
Generic function
plot.nest
Scree plot of the eigenvalues and the simulated confidence intervals for alpha
.
loadings
Extract loadings. It does not overwrite stat::loadings
.
summary.nest
Summary statistics for the number of factors.
Author(s)
P.-O. Caron
References
Achim, A. (2017). Testing the number of required dimensions in exploratory factor analysis. The Quantitative Methods for Psychology, 13(1), 64-74. doi:10.20982/tqmp.13.1.p064
Examples
nest(ex_2factors, n = 100)
nest(mtcars)