powerNLSEM: powerNLSEM function

In powerNLSEM: Simulation-Based Power Estimation (MSPE) for Nonlinear SEM

powerNLSEM function

Description

powerNLSEM function

Usage

powerNLSEM(
  model,
  POI,
  method,
  test = "onesided",
  power_modeling_method = "probit",
  search_method = "adaptive",
  R = 2000,
  power_aim = 0.8,
  alpha = 0.05,
  alpha_power_modeling = 0.05,
  CORES = max(c(parallel::detectCores() - 2, 1)),
  verbose = TRUE,
  seed = NULL,
  ...
)

Arguments

model	Model in lavaan syntax. See documentation for help and examples.
POI	Parameter Of Interest as a vector of strings. Must be in lavaan-syntax without any spaces. Nonlinear effects should have the same ordering as in model.
method	Method used to fit to the data. Implemented methods are "LMS" (Klein & Moosbrugger, 2000) (requires an installation of Mplus and the MplusAutomation pacakge), "UPI" (Kelava & Brandt, 2009, Marsh et al., 2004) for the unconstrained product indicator approach, "FSR" (Ng and Chan, 2020) for the naïve factor score approach, and "SR", for using scale means (i.e., scale regression/path modeling).
test	Should the parameter be tested with a directed hypothesis (onesided) or with an undirected hypothesis (twosided, also equivalent to Wald-Test for single parameter). Default to "onesided".
power_modeling_method	Power modeling method used to model significant parameter estimates. Default to "probit" indicating glm with probit link function with sqrt(n) as predictor. Alternative is "logit".
search_method	String stating the search method. Default to "adaptive" (synonyme is "smart"). Alternative is "bruteforce".
R	Total number of models to be fitted. Higher number results in higher precision and longer runtime. Default to 2000.
power_aim	Minimal power value to approximate. Default to .8.
alpha	Type I-error rate for significance decision. Default to .05.
alpha_power_modeling	Type I-error rate for confidence band around predicted power rate. Used to ensure that the computed N keeps the desired power value (with the given Type I-error rate alpha_power_modeling divided by 2). If set to 1, no confidence band is used. Default to .05.
CORES	Number of cores used for parallelization. Default to number of available cores - 2.
verbose	Logical whether progress should be printed in console. Default to TRUE.
seed	Seed for replicability. Default to NULL, then a seed is drawn at random, which will also be saved in the output.
...	Additional arguments passed on to the search functions.

Value

Returns an list object of class powerNLSEM.

References

Klein, A. G., & Moosbrugger, H. (2000). Maximum likelihood estimation of latent interaction effects with the LMS method. Psychometrika, 65(4), 457–474. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1007/BF02296338")}

Kelava, A., & Brandt, H. (2009). Estimation of nonlinear latent structural equation models using the extended unconstrained approach. Review of Psychology, 16(2), 123–132.

Lin, G. C., Wen, Z., Marsh, H. W., & Lin, H. S. (2010). Structural equation models of latent interactions: Clarification of orthogonalizing and double-mean-centering strategies. Structural Equation Modeling, 17(3), 374–391. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1080/10705511.2010.488999")}

Little, T. D., Bovaird, J. A., & Widaman, K. F. (2006). On the merits of orthogonalizing powered and product terms: Implications for modeling interactions among latent variables. Structural Equation Modeling, 13(4), 497–519. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1207/s15328007sem1304_1")}

Marsh, H. W., Wen, Z. & Hau, K. T. (2004). Structural equation models of latent interactions: Evaluation of alternative estimation strategies and indicator construction. Psychological Methods, 9(3), 275–300. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1037/1082-989X.9.3.275")}

Ng, J. C. K., & Chan, W. (2020). Latent moderation analysis: A factor score approach. Structural Equation Modeling: A Multidisciplinary Journal, 27(4), 629–648. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1080/10705511.2019.1664304")}.

Irmer, J. P., Klein, A. G., & Schermelleh-Engel, K. (2024a). A General Model-Implied Simulation-Based Power Estimation Method for Correctly and Misspecfied Models: Applications to Nonlinear and Linear Structural Equation Models. Behavior Research Methods. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.31219/osf.io/pe5bj")}

Irmer, J. P., Klein, A. G., & Schermelleh-Engel, K. (2024b). Estimating Power in Complex Nonlinear Structural Equation Modeling Including Moderation Effects: The ⁠powerNLSEM R⁠-Package. Behavior Research Methods. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.3758/s13428-024-02476-3")}

See Also

For further details for specific uses see corresponding functions: power_search() for all inputs possible, UPI() for specifics for the unconstrained product indicator approach, LMS() for the latent moderated structural equations approach, FSR() for factor score approaches, SR() for scale regression approaches.

Examples

# write model in lavaan syntax
model <- "
# measurement models
          X =~ 1*x1 + 0.8*x2 + 0.7*x3
          Y =~ 1*y1 + 0.85*y2 + 0.78*y3
          Z =~ 1*z1 + 0.9*z2 + 0.6*z3

# structural models
          Y ~ 0.3*X + .2*Z +  .2*X:Z

# residual variances
         Y~~.7975*Y
         X~~1*X
         Z~~1*Z

# covariances
         X~~0.5*Z

# measurement error variances
         x1~~.1*x1
         x2~~.2*x2
         x3~~.3*x3
         z1~~.2*z1
         z2~~.3*z2
         z3~~.4*z3
         y1~~.5*y1
         y2~~.4*y2
         y3~~.3*y3
"
# run model-implied simulation-based power estimation
# for the effects: c("Y~X", "Y~Z", "Y~X:Z")
Result_Power <- powerNLSEM(model = model, POI = c("Y~X", "Y~Z", "Y~X:Z"),
                           method = "UPI", search_method = "adaptive",
                           steps = 10, power_modeling_method = "probit",
                           R = 1000, power_aim = .8, alpha = .05,
                           alpha_power_modeling = .05,
                           CORES = 1, seed = 2024)

Result_Power

powerNLSEM documentation built on Sept. 27, 2024, 5:10 p.m.