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), or "both" (i.e., both measurement and structural tables).
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 gt::gt() table object(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(). writeTo is only relevant if gtTab = TRUE.
fileName: A character string specifying a desired base name for the output gt::gt() file(s). If a fileName is not provided (i.e., fileName = NULL), then defaults will be used (e.g., "dySEM_table", "dySEM_table_measurement", or "dySEM_table_structural) based on the tabletype argument. The resulting base name will automatically be appended with a .rtf file extension. fileName is only relevant if gtTab = TRUE and writeTo is specified.

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