Schmid-Leiman transformation

SL() has been superseded by efa_schmid_leiman(), which is the recommended interface going forward. It remains available and unchanged so existing code keeps working.

Usage

SL(
  x,
  Phi = NULL,
  type = c("EFAtools", "psych", "SPSS", "none"),
  method = c("PAF", "ML", "ULS", "MINRES"),
  g_name = "g",
  ...
)

Arguments

x

object of class efa_fit(), class psych::fa(), class lavaan::lavaan(), a matrix, or an efa_loadings/loadings object. If class efa_fit() or class psych::fa(), pattern coefficients and factor intercorrelations are taken from this object. If class lavaan::lavaan(), it must be a second-order CFA solution. In this case first-order and second-order factor loadings are taken from this object and the g_name argument has to be specified. x can also be a pattern matrix from an oblique factor solution (see Phi).

Phi

matrix. A matrix of factor intercorrelations from an oblique factor solution. Only needs to be specified if a pattern matrix is entered directly into x.

type

character. One of "EFAtools" (default), "psych", "SPSS", or "none". This is used to control the procedure of the second-order factor analysis. In efa_schmid_leiman() it is set through the type of the estimate_control() object.

method

character. The estimator for the second-order factor analysis; passed to efa_schmid_leiman() as its estimator argument. One of "PAF", "ML", "ULS", or "MINRES".

g_name

character. The name of the general factor. This needs only be specified if x is a lavaan second-order solution. Default is "g".

...

Further arguments passed on to the second-order efa_fit(), including the estimation tuning knobs (init_comm, criterion, criterion_type, max_iter, abs_eigen, start_method), which are repacked, together with type, into an estimate_control() object so that they tune that fit exactly as they always did. The estimator is selected with method.

Value

A list of class c("efa_schmid_leiman", "SL"), identical to the value of efa_schmid_leiman(); see there for the components.

See also

efa_schmid_leiman()