A Function That Exports Tables based on dySEM models

This function takes the model from fitted dySEM scripts and creates tables of expected output and/or exports them as .rtfs.

Usage

outputParamTab(
  dvn,
  model = NULL,
  fit,
  tabletype = NULL,
  gtTab = FALSE,
  writeTo = NULL,
  fileName = NULL
)

Arguments

dvn: Input dvn list from scrapeVarCross()
model: A character input specifying type of model to output: "cfa", "bidyc", "apim", "mim", "cfm", or "bidys".
fit: input object from fitted lavaan model.
tabletype: A character input of what type of table(s) is(are) desired. Options are "measurement" (i.e., loadings, intercepts, etc.), "structural" (i.e., latent slopes, such as actor/partner effects, k parameters), "both" (i.e., both measurement and structural tables), or "correlation" (for a table of factor correlations).
gtTab: A logical input indicating whether to generate the table(s) in gt::gt() table object format (TRUE). By default (FALSE), the table(s) are generated in tibble::tibble() format. Users can also apply the writeTo argument if they wish to export the gt::gt() table object(s).
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 tibble::tibble() if gtTab = FALSE (default), or gt::gt() object if gtTab = TRUE, of specified model parameter estimates and corresponding statistical tests.

Details

If gtTab = TRUE and writeTo is specified, then output will simultaneously be saved as a .rtf file to the user's specified directory.
If output file(s) is(are) successfully saved, a confirmation message will be printed to the console.
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
)

outputParamTab(dvnx,
  model = "cfa", sat.config.mod, tabletype = "measurement",
  writeTo = tempdir(), fileName = "dCFA_configural"
)
#> # A tibble: 10 × 8
#>    `Latent Factor` Indicator Loading    SE     Z `p-value` `Std. Loading`
#>    <chr>           <chr>       <dbl> <dbl> <dbl> <chr>              <dbl>
#>  1 Sat1            sat.g.1_1    2.11 0.158  13.3 < .001             0.937
#>  2 Sat1            sat.g.1_2    1.91 0.168  11.4 < .001             0.851
#>  3 Sat1            sat.g.1_3    2.10 0.161  13.1 < .001             0.926
#>  4 Sat1            sat.g.1_4    1.96 0.162  12.0 < .001             0.884
#>  5 Sat1            sat.g.1_5    1.87 0.179  10.4 < .001             0.806
#>  6 Sat2            sat.g.2_1    1.83 0.144  12.7 < .001             0.91 
#>  7 Sat2            sat.g.2_2    1.84 0.146  12.7 < .001             0.908
#>  8 Sat2            sat.g.2_3    1.88 0.146  12.9 < .001             0.918
#>  9 Sat2            sat.g.2_4    1.61 0.151  10.7 < .001             0.819
#> 10 Sat2            sat.g.2_5    1.93 0.159  12.1 < .001             0.887
#> # ℹ 1 more variable: Intercept <dbl>

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
)

outputParamTab(dvnxy,
  model = "cfa", sat.config.mod, tabletype = "measurement",
  writeTo = tempdir(), fileName = "APIM_indist"
)
#> NULL