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A Function That Writes, Saves, and Exports Syntax for Fitting Boutique Two-Construct Dyadic SEM Models

Source: R/scriptTwoCross.R
scriptTwoCross.Rd

This function takes the outputted object from scrapeVarCross() with both X and Y indicators and automatically writes, returns, and exports (.txt) lavaan syntax for specifying a two-construct model where the measurement structure of X and Y can differ (e.g., X as correlated factors, Y as unidimensional).

Usage

scriptTwoCross(
  dvn,
  x_scaleset = "FF",
  y_scaleset = "FF",
  lvxname = "X",
  lvyname = "Y",
  x_model = "cor",
  y_model = "cor",
  constr_dy_x_meas = c("loadings", "intercepts", "residuals"),
  constr_dy_x_struct = c("variances", "means"),
  constr_dy_y_meas = c("loadings", "intercepts", "residuals"),
  constr_dy_y_struct = c("variances", "means"),
  constr_dy_xy_struct = "free",
  includeMeanStruct = FALSE,
  writeTo = NULL,
  fileName = NULL
)

Arguments

dvn

Input dvn list from scrapeVarCross() containing information for both X and Y (i.e., must include both x_stem and y_stem arguments when calling scrapeVarCross).

x_scaleset

Character string specifying how to set the scale of latent X. Options are "FF" (fixed-factor) or "MV" (marker variable). Default is "FF".

y_scaleset

Character string specifying how to set the scale of latent Y. Options are "FF" (fixed-factor) or "MV" (marker variable). Default is "FF".

lvxname

Input character to (arbitrarily) name latent variable X in lavaan syntax.

lvyname

Input character to (arbitrarily) name latent variable Y in lavaan syntax.

x_model

Character string specifying the measurement model for X. Options are "uni" (unidimensional), "cor" (correlated factors), "hier" (hierarchical), or "bifactor".

y_model

Character string specifying the measurement model for Y. Same options as x_model.

constr_dy_x_meas

Input character vector detailing which measurement model parameters to constrain across dyad members for latent X.

constr_dy_x_struct

Input character vector detailing which structural model parameters to constrain across dyad members for latent X.

constr_dy_y_meas

Input character vector detailing which measurement model parameters to constrain across dyad members for latent Y.

constr_dy_y_struct

Input character vector detailing which structural model parameters to constrain across dyad members for latent Y.

constr_dy_xy_struct

Character string for the X-to-Y regression paths. Options are "free" (default; all paths freely estimated) or "zero" (all paths fixed to zero).

includeMeanStruct

Logical for whether to include the mean structure (intercepts and latent means) in the model. Defaults to FALSE.

writeTo

A character string specifying a directory path to where the output file(s) should be saved. If set to ".", the file(s) will be written to the current working directory. The default is NULL, and examples use a temporary directory created by tempdir(). When dealing with tabular output, writeTo is only relevant if gtTab = TRUE.

fileName

A character string specifying a desired base name for the output file. The default is NULL. The specified name will be automatically appended with the appropriate file extension (e.g., .txt for lavaan scripts, .rtf for tabular output when gtTab = TRUE, or other extensions as appropriate for the output type). If a file with the same name already exists in the user's chosen directory, it will be overwritten.

Value

Character object of lavaan script that can be passed immediately to lavaan functions.

See also

scrapeVarCross which this function relies on; scriptUni, scriptCor, scriptHier, scriptBifac for the underlying scripters; scriptAPIM for a related two-construct function.

Other bi-construct script-writing functions: scriptAPIM(), scriptBiDy(), scriptCFM(), scriptMIM()

Examples

dvn <- scrapeVarCross(
  dat = commitmentQ,
  x_order = "spi", x_stem = "sat.g", x_delim1 = ".", x_delim2 = "_",
  distinguish_1 = "1", distinguish_2 = "2",
  y_order = "spi", y_stem = "com", y_delim1 = ".", y_delim2 = "_"
)
#> 
#> ── Variable Scraping Summary ──
#> 
#> ✔ Successfully scraped 2 latent variables: sat.g and com
#> ℹ sat.g: 5 indicators for P1 (1), 5 indicators for P2 (2)
#> ℹ com: 5 indicators for P1 (1), 5 indicators for P2 (2)
#> ℹ Total indicators: 20

# Correlated X, unidimensional Y
scriptTwoCross(dvn, lvxname = "Sat", lvyname = "Com",
  x_model = "cor", y_model = "uni")
#> [1] "#Measurement Model\n\n#Loadings\nSat1=~NA*sat.g.1_1+lx1*sat.g.1_1+lx2*sat.g.1_2+lx3*sat.g.1_3+lx4*sat.g.1_4+lx5*sat.g.1_5\nSat2=~NA*sat.g.2_1+lx1*sat.g.2_1+lx2*sat.g.2_2+lx3*sat.g.2_3+lx4*sat.g.2_4+lx5*sat.g.2_5\n\nComDy=~NA*com.1_1+lyg1*com.1_1+lyg2*com.1_2+lyg3*com.1_3+lyg4*com.1_4+lyg5*com.1_5+lgy1*com.2_1+lgy2*com.2_2+lgy3*com.2_3+lgy4*com.2_4+lgy5*com.2_5\n\n#Residual Variances\nsat.g.1_1 ~~ thx1*sat.g.1_1\nsat.g.1_2 ~~ thx2*sat.g.1_2\nsat.g.1_3 ~~ thx3*sat.g.1_3\nsat.g.1_4 ~~ thx4*sat.g.1_4\nsat.g.1_5 ~~ thx5*sat.g.1_5\nsat.g.2_1 ~~ thx1*sat.g.2_1\nsat.g.2_2 ~~ thx2*sat.g.2_2\nsat.g.2_3 ~~ thx3*sat.g.2_3\nsat.g.2_4 ~~ thx4*sat.g.2_4\nsat.g.2_5 ~~ thx5*sat.g.2_5\n\ncom.1_1 ~~ thy1*com.1_1\ncom.1_2 ~~ thy2*com.1_2\ncom.1_3 ~~ thy3*com.1_3\ncom.1_4 ~~ thy4*com.1_4\ncom.1_5 ~~ thy5*com.1_5\ncom.2_1 ~~ thy1*com.2_1\ncom.2_2 ~~ thy2*com.2_2\ncom.2_3 ~~ thy3*com.2_3\ncom.2_4 ~~ thy4*com.2_4\ncom.2_5 ~~ thy5*com.2_5\n\n#Residual Covariances\nsat.g.1_1 ~~ sat.g.2_1\nsat.g.1_2 ~~ sat.g.2_2\nsat.g.1_3 ~~ sat.g.2_3\nsat.g.1_4 ~~ sat.g.2_4\nsat.g.1_5 ~~ sat.g.2_5\n\ncom.1_1 ~~ com.2_1\ncom.1_2 ~~ com.2_2\ncom.1_3 ~~ com.2_3\ncom.1_4 ~~ com.2_4\ncom.1_5 ~~ com.2_5\n\n#Structural Model\n\n#Latent (Co)Variances\nSat1 ~~ 1*Sat1 + psix*Sat1\nSat2 ~~ psix*Sat2\nSat1 ~~ Sat2\n\nComDy ~~ 1*ComDy\n\n#Latent Regressions (X -> Y)\nComDy ~ b_ComDy_Sat1*Sat1 + b_ComDy_Sat2*Sat2"

# Both correlated
scriptTwoCross(dvn, lvxname = "Sat", lvyname = "Com",
  x_model = "cor", y_model = "cor",
  writeTo = tempdir(), fileName = "twoCross_cor_cor")
#> [1] "#Measurement Model\n\n#Loadings\nSat1=~NA*sat.g.1_1+lx1*sat.g.1_1+lx2*sat.g.1_2+lx3*sat.g.1_3+lx4*sat.g.1_4+lx5*sat.g.1_5\nSat2=~NA*sat.g.2_1+lx1*sat.g.2_1+lx2*sat.g.2_2+lx3*sat.g.2_3+lx4*sat.g.2_4+lx5*sat.g.2_5\n\nCom1=~NA*com.1_1+ly1*com.1_1+ly2*com.1_2+ly3*com.1_3+ly4*com.1_4+ly5*com.1_5\nCom2=~NA*com.2_1+ly1*com.2_1+ly2*com.2_2+ly3*com.2_3+ly4*com.2_4+ly5*com.2_5\n\n#Residual Variances\nsat.g.1_1 ~~ thx1*sat.g.1_1\nsat.g.1_2 ~~ thx2*sat.g.1_2\nsat.g.1_3 ~~ thx3*sat.g.1_3\nsat.g.1_4 ~~ thx4*sat.g.1_4\nsat.g.1_5 ~~ thx5*sat.g.1_5\nsat.g.2_1 ~~ thx1*sat.g.2_1\nsat.g.2_2 ~~ thx2*sat.g.2_2\nsat.g.2_3 ~~ thx3*sat.g.2_3\nsat.g.2_4 ~~ thx4*sat.g.2_4\nsat.g.2_5 ~~ thx5*sat.g.2_5\n\ncom.1_1 ~~ thy1*com.1_1\ncom.1_2 ~~ thy2*com.1_2\ncom.1_3 ~~ thy3*com.1_3\ncom.1_4 ~~ thy4*com.1_4\ncom.1_5 ~~ thy5*com.1_5\ncom.2_1 ~~ thy1*com.2_1\ncom.2_2 ~~ thy2*com.2_2\ncom.2_3 ~~ thy3*com.2_3\ncom.2_4 ~~ thy4*com.2_4\ncom.2_5 ~~ thy5*com.2_5\n\n#Residual Covariances\nsat.g.1_1 ~~ sat.g.2_1\nsat.g.1_2 ~~ sat.g.2_2\nsat.g.1_3 ~~ sat.g.2_3\nsat.g.1_4 ~~ sat.g.2_4\nsat.g.1_5 ~~ sat.g.2_5\n\ncom.1_1 ~~ com.2_1\ncom.1_2 ~~ com.2_2\ncom.1_3 ~~ com.2_3\ncom.1_4 ~~ com.2_4\ncom.1_5 ~~ com.2_5\n\n#Structural Model\n\n#Latent (Co)Variances\nSat1 ~~ 1*Sat1 + psix*Sat1\nSat2 ~~ psix*Sat2\nSat1 ~~ Sat2\n\nCom1 ~~ 1*Com1 + psiy*Com1\nCom2 ~~ psiy*Com2\nCom1 ~~ Com2\n\n#Latent Regressions (X -> Y)\nCom1 ~ b_Com1_Sat1*Sat1 + b_Com1_Sat2*Sat2\nCom2 ~ b_Com2_Sat1*Sat1 + b_Com2_Sat2*Sat2"