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Write Population lavaan Syntax for Correlated Dyadic CFA

Source: R/scriptPopCor.R
scriptPopCor.Rd

Produces lavaan syntax with fixed numeric values for data generation in Monte Carlo simulations. Used with lavaan::simulateData().

Usage

scriptPopCor(dvn, lvname = "X", pop_values, lvar = "X")

Arguments

dvn

Input dvn list from build_dvn() or scrapeVarCross()

lvname

Character name for the latent variable

pop_values

Named list of population parameter values:

  • loadings_p1: numeric vector for partner 1 (length = n_items)

  • loadings_p2: numeric vector for partner 2 (length = n_items). If NULL, uses loadings_p1.

  • residuals_p1: numeric vector (length = n_items)

  • residuals_p2: numeric vector (length = n_items). If NULL, uses residuals_p1.

  • coresids: numeric vector (length = n_items)

  • intercepts_p1: optional (default 0)

  • intercepts_p2: optional (default 0)

  • lv_var: latent variances; scalar (both=1) or length-2 vector

  • lv_cov: latent covariance

  • lv_means: latent means; scalar (both=0) or length-2 vector

lvar

Character "X" or "Y". Default "X".

Value

Character string of lavaan syntax

See also

Other population-scripting functions: scriptPopUni()

Examples

dvn <- dySEM::build_dvn(n_items_x = 3, x_stem = "x", x_delim1 = "", x_delim2 = "_",
  distinguish_1 = "A", distinguish_2 = "B")
scriptPopCor(dvn, "X", list(
  loadings_p1 = c(.75, .80, .55),
  loadings_p2 = c(.70, .85, .50),
  residuals_p1 = c(.44, .36, .70),
  residuals_p2 = c(.51, .28, .75),
  coresids = c(.10, .12, .08),
  lv_cov = 0.3
))
#> [1] "# Correlated Dyadic CFA - Population Model\n\n# Loadings\nXA =~ 0.75*x1_A + 0.8*x2_A + 0.55*x3_A\nXB =~ 0.7*x1_B + 0.85*x2_B + 0.5*x3_B\n\n# Intercepts\nx1_A ~ 0*1\nx2_A ~ 0*1\nx3_A ~ 0*1\nx1_B ~ 0*1\nx2_B ~ 0*1\nx3_B ~ 0*1\n\n# Residual Variances\nx1_A ~~ 0.44*x1_A\nx2_A ~~ 0.36*x2_A\nx3_A ~~ 0.7*x3_A\nx1_B ~~ 0.51*x1_B\nx2_B ~~ 0.28*x2_B\nx3_B ~~ 0.75*x3_B\n\n# Residual Covariances\nx1_A ~~ 0.1*x1_B\nx2_A ~~ 0.12*x2_B\nx3_A ~~ 0.08*x3_B\n\n# Latent Variances\nXA ~~ 1*XA\nXB ~~ 1*XB\n\n# Latent Covariance\nXA ~~ 0.3*XB\n\n# Latent Means\nXA ~ 0*1\nXB ~ 0*1"