obj_func: Objective function for optimizing RMSEA and CFI

In JustinKracht/noisemaker: Simulate population correlation matrices with model error

Objective function for optimizing RMSEA and CFI

Description

This is the objective function that is minimized by the tkl function.

Usage

obj_func(
  par = c(v, eps),
  Rpop,
  W,
  p,
  u,
  df,
  target_rmsea,
  target_cfi,
  weights = c(1, 1),
  WmaxLoading = NULL,
  NWmaxLoading = 2,
  penalty = 0,
  return_values = FALSE
)

Arguments

par	(vector) Values of model error variance (\nu_{\textrm{e}}) and epsilon (\epsilon).
Rpop	(matrix) The model-implied correlation matrix.
W	(matrix) Matrix of provisional minor common factor loadings with unit column variances.
p	(scalar) Number of variables.
u	(vector) Major common factor variances.
df	(scalar) Model degrees of freedom.
target_rmsea	(scalar) Target RMSEA value.
target_cfi	(scalar) Target CFI value.
weights	(vector) Vector of length two indicating how much weight to give RMSEA and CFI, e.g., c(1,1) (default) gives equal weight to both indices; c(1,0) ignores the CFI value.
WmaxLoading	(scalar) Threshold value for NWmaxLoading.
NWmaxLoading	(scalar) Maximum number of absolute loadings \ge WmaxLoading in any column of W.
penalty	(scalar) Large (positive) penalty value to apply if the NWmaxLoading condition is violated.
return_values	(boolean) If TRUE, return the objective function value along with Rpop, RpopME, W, RMSEA, CFI, v, and eps values. If FALSE, return only the objective function value.

JustinKracht/noisemaker documentation built on Jan. 29, 2024, 1:26 a.m.