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A Function That Fits and Compares Competing Dyadic Uni-construct Models

Source: R/outputUniConstructComp.R
outputUniConstructComp.Rd

This function takes the outputted object from scrapeVarCross() along with the corresponding dataset and automatically tests competing uni-construct dyadic models for the latent variable under consideration. It inspects four possible model variants:

Usage

outputUniConstructComp(
  dvn,
  dat,
  indexes = c("df", "chisq", "cfi", "rmsea", "bic", "GenTEFI"),
  ...,
  gtTab = FALSE,
  writeTo = NULL,
  fileName = NULL
)

Arguments

dvn

Input dvn list from scrapeVarCross().

dat

Input data frame containing the dataset for model estimation.

indexes

Input character vector specifying which index(es) to return. Default is c("df", "chisq", "cfi", "rmsea", "bic", "GenTEFI"). Note:

  • Valid entries include "GenTEFI"—the Generalized Total Entropy Fit Index (see Golino et al., 2024)—and those from lavaan::fitMeasures().

  • If "chisq" is entered, chi-squared difference tests are automatically performed via lavaan::lavTestLRT(), and the resulting p-values are added to the output.

...

Additional arguments to be passed to lavaan::cfa(), allowing users to customize model estimation settings. By default, the models are fit with maximum-likelihood estimation (estimator = "ml") and missing data are handled via listwise deletion (missing = "listwise"), as per lavaan::cfa()'s default behaviour.

gtTab

A logical input indicating whether to generate the requested index(es) for each fitted model (requested via the indexes argument) in gt::gt() table object format (TRUE). Users can also apply the writeTo argument if they wish to export the gt::gt() table object.

writeTo

A character vector string specifying a directory path to where the gt::gt() table object should be saved. If set to ".", the file 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. 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 list containing up to two components:

  • Indexes: A tibble::tibble() if gtTab = FALSE (default), or gt::gt() object if gtTab = TRUE, with the desired index(es) for each fitted model (requested via the indexes argument).

  • GenTEFI: A tibble::tibble() of the GenTEFI (if "GenTEFI" is included in the indexes argument).

Details

  • If "chisq" is included in indexes, the specific form of the applied chi-squared difference test (e.g., standard vs. robust) is determined automatically by lavaan::lavTestLRT(), based on the model estimation method used.

  • 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 is 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

dvn <- scrapeVarCross(
commitmentM,
x_order = "sip",
x_stem = "sat.g",
x_delim1 = "",
x_delim2 = "_",
distinguish_1 = "f",
distinguish_2 = "m"
)
#> 
#> ── Variable Scraping Summary ──
#> 
#> ✔ Successfully scraped 1 latent variable: sat.g
#> ℹ sat.g: 5 indicators for P1 (f), 5 indicators for P2 (m)
#> ℹ Total indicators: 10

# Quick example for CRAN checks
outputUniConstructComp(
  dvn,
  commitmentM,
  indexes = c("df", "bic"),
  missing = "listwise"
)
#> # A tibble: 4 × 6
#>   Model              Type            df   bic df_diff bic_diff
#>   <chr>              <chr>        <dbl> <dbl>   <dbl>    <dbl>
#> 1 Bifactor           Higher Order    20 8374.      NA     NA  
#> 2 Hierarchical       Higher Order    29 8336.       9    -38.2
#> 3 Correlated Factors Lower Order     29 8336.      NA     NA  
#> 4 Unidimensional     Lower Order     30 9316.       1    980. 

# \donttest{
# More comprehensive examples (slower due to FIML estimation)
outputUniConstructComp(
  dvn,
  commitmentM,
  missing = "fiml"
)
#> According to GenTEFI (-15.564),
#>           higher-order models (e.g., bifactor or hierarchical) fit the data better than lower-order models (e.g., correlated factors),
#>           as the higher-order TEFI (-9.606) is smaller than the lower-order TEFI (-5.959).
#> $Indexes
#> # A tibble: 4 × 14
#>   Model         Type  genTEFI    df  chisq   cfi  rmsea   bic df_diff chisq_diff
#>   <chr>         <chr> <chr>   <dbl>  <dbl> <dbl>  <dbl> <dbl>   <dbl>      <dbl>
#> 1 Bifactor      High… Yes        20   44.8 0.994 0.0663 8512.      NA       NA  
#> 2 Hierarchical  High… Yes        29   56.8 0.993 0.0583 8474.       9       12.0
#> 3 Correlated F… Lowe… No         29   56.8 0.993 0.0583 8474.      NA       NA  
#> 4 Unidimension… Lowe… No         30 1048.  0.743 0.347  9459.       1      991. 
#> # ℹ 4 more variables: chisq_diff_test_p <dbl>, cfi_diff <dbl>,
#> #   rmsea_diff <dbl>, bic_diff <dbl>
#> 
#> $`GenTEFI Details`
#> # A tibble: 1 × 3
#>   VN.Entropy.Fit Level_1_VN Level_2_VN
#>            <dbl>      <dbl>      <dbl>
#> 1          -15.6      -5.96      -9.61
#> 

outputUniConstructComp(
  dvn,
  commitmentM,
  indexes = c("df", "bic"),
  missing = "fiml"
)
#> # A tibble: 4 × 6
#>   Model              Type            df   bic df_diff bic_diff
#>   <chr>              <chr>        <dbl> <dbl>   <dbl>    <dbl>
#> 1 Bifactor           Higher Order    20 8512.      NA     NA  
#> 2 Hierarchical       Higher Order    29 8474.       9    -38.8
#> 3 Correlated Factors Lower Order     29 8474.      NA     NA  
#> 4 Unidimensional     Lower Order     30 9459.       1    985. 

outputUniConstructComp(
  dvn,
  commitmentM,
  indexes = c("df", "bic"),
  estimator = "ml",
  missing = "fiml"
)
#> # A tibble: 4 × 6
#>   Model              Type            df   bic df_diff bic_diff
#>   <chr>              <chr>        <dbl> <dbl>   <dbl>    <dbl>
#> 1 Bifactor           Higher Order    20 8512.      NA     NA  
#> 2 Hierarchical       Higher Order    29 8474.       9    -38.8
#> 3 Correlated Factors Lower Order     29 8474.      NA     NA  
#> 4 Unidimensional     Lower Order     30 9459.       1    985. 

outputUniConstructComp(
  dvn,
  commitmentM,
  indexes = c("df", "bic"),
  missing = "fiml",
  gtTab = TRUE,
  writeTo = tempdir(),
  fileName = "uni-construct-dyad-models"
)
#> Output stored in: /tmp/RtmpfSfs3l/uni-construct-dyad-models.rtf



  
Model
      Type
      df
      bic
      df_diff
      bic_diff
    
Bifactor
Higher Order
20
8512.446
NA
NA
Hierarchical
Higher Order
29
8473.677
9
-38.76866
Correlated Factors
Lower Order
29
8473.677
NA
NA
Unidimensional
Lower Order
30
9459.109
1
985.43122
# }