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A function That Exports SEM diagrams based on dySEM models

Source: R/outputParamFig.R
outputParamFig.Rd

This function takes the model from fitted dySEM scripts and exports .png path diagram figures of expected output.

Usage

outputParamFig(fit, figtype = NULL, writeTo = NULL, fileName = NULL)

Arguments

fit

Input object from fitted lavaan model.

figtype

A character input of what type of model is desired:

  • "unstandardized" for unstandardized path coefficients.

  • "standardized" for standardized path coefficients.

  • "labels" for labelled parameters.

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

A qgraph object of the desired SEM path diagram, which is simultaneously exported as a .png file to the specified directory.

Details

  • The function uses semPlot::semPaths() to create a qgraph object of the desired SEM path diagram.

  • After execution, a semPlot::semPaths() message will be printed to the console confirming the directory path of the saved output file.

  • If a file with the same name already exists in the user's chosen directory, it will be overwritten.

Examples

dvnx <- scrapeVarCross(
  dat = commitmentQ, x_order = "spi", x_stem = "sat.g", x_delim1 = ".",
  x_delim2 = "_", distinguish_1 = "1", distinguish_2 = "2"
)
#> 
#> ── Variable Scraping Summary ──
#> 
#> ✔ Successfully scraped 1 latent variable: sat.g
#> ℹ sat.g: 5 indicators for P1 (1), 5 indicators for P2 (2)
#> ℹ Total indicators: 10

sat.config.script <- scriptCor(dvnx,
  lvname = "Sat", constr_dy_meas = "none",
  constr_dy_struct = "none"
)

sat.config.mod <- lavaan::cfa(sat.config.script,
  data = commitmentQ, std.lv = FALSE,
  auto.fix.first = FALSE, meanstructure = TRUE
)

outputParamFig(sat.config.mod,
  figtype = "standardized",
  writeTo = tempdir(), fileName = "dCFA_configural"
)
#> Output stored in /home/runner/work/dySEM/dySEM/docs/reference//tmp/RtmpIVCISQ/dCFA_configural std.png

dvnxy <- 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

apim.indist.script <- scriptAPIM(dvnxy, lvxname = "Sat", lvyname = "Com", est_k = TRUE)

apim.indist.mod <- lavaan::cfa(apim.indist.script,
  data = commitmentQ, std.lv = FALSE,
  auto.fix.first = FALSE, meanstructure = TRUE
)

outputParamFig(apim.indist.mod,
  figtype = "standardized",
  writeTo = tempdir(), fileName = "APIM_indist"
)
#> Output stored in /home/runner/work/dySEM/dySEM/docs/reference//tmp/RtmpIVCISQ/APIM_indist std.png