NEST() has been superseded by efa_nest(), which is the recommended
interface going forward. It remains available and unchanged so existing code
keeps working.
NEST(
x,
N = NA,
alpha = 0.05,
use = c("pairwise.complete.obs", "all.obs", "complete.obs", "everything",
"na.or.complete"),
cor_method = c("pearson", "spearman", "kendall", "poly", "tetra"),
n_datasets = 1000,
...
)An object of class efa_retention, identical to the value of
efa_nest(); see there for the components.
data.frame or matrix. Dataframe or matrix of raw data or matrix with correlations.
numeric. The number of observations. Only needed if x is a correlation matrix. Must be larger than the number of variables.
numeric. The alpha level to use (i.e., 1-alpha percentile of eigenvalues is used for reference values).
character. Passed to stats::cor() if raw
data is given as input. Default is "pairwise.complete.obs".
character. One of "pearson", "spearman", or "kendall",
passed to stats::cor(). "poly" and "tetra" are not supported because
NEST compares the data against simulated continuous reference data.
Default is "pearson".
numeric. The number of datasets to simulate. Default is 1000.
Further arguments passed on to the efa_fit() fits. For example,
estimator, to change the estimator (PAF is default), or one of the estimation
tuning knobs (type, init_comm, criterion, criterion_type, max_iter,
abs_eigen, start_method), which are repacked into an estimate_control()
object so that they tune the fits exactly as they always did.
efa_nest()